id	sid	tid	token	lemma	pos
ajst-31195	1	1	academic	academic	ADJ
ajst-31195	1	2	journal	journal	NOUN
ajst-31195	1	3	of	of	ADP
ajst-31195	1	4	science	science	NOUN
ajst-31195	1	5	and	and	CCONJ
ajst-31195	1	6	technology	technology	NOUN
ajst-31195	1	7	issn	issn	NOUN
ajst-31195	1	8	:	:	PUNCT
ajst-31195	1	9	2771	2771	NUM
ajst-31195	1	10	-	-	SYM
ajst-31195	1	11	3032	3032	NUM
ajst-31195	1	12	|	|	NOUN
ajst-31195	1	13	vol	vol	NOUN
ajst-31195	1	14	.	.	PROPN
ajst-31195	1	15	15	15	NUM
ajst-31195	1	16	,	,	PUNCT
ajst-31195	1	17	no	no	INTJ
ajst-31195	1	18	.	.	NOUN
ajst-31195	1	19	3	3	NUM
ajst-31195	1	20	,	,	PUNCT
ajst-31195	1	21	2025	2025	NUM
ajst-31195	1	22	17	17	NUM
ajst-31195	1	23	feature	feature	NOUN
ajst-31195	1	24	recognition	recognition	NOUN
ajst-31195	1	25	and	and	CCONJ
ajst-31195	1	26	modeling	model	VERB
ajst-31195	1	27	analysis	analysis	NOUN
ajst-31195	1	28	of	of	ADP
ajst-31195	1	29	apple	apple	NOUN
ajst-31195	1	30	images	image	NOUN
ajst-31195	1	31	based	base	VERB
ajst-31195	1	32	on	on	ADP
ajst-31195	1	33	yolov8x‐seg	yolov8x‐seg	PROPN
ajst-31195	1	34	training	training	NOUN
ajst-31195	1	35	model	model	NOUN
ajst-31195	1	36	haimei	haimei	PROPN
ajst-31195	1	37	lu1	lu1	PROPN
ajst-31195	1	38	,	,	PUNCT
ajst-31195	1	39	yuxin	yuxin	PROPN
ajst-31195	1	40	zhang2	zhang2	PROPN
ajst-31195	1	41	,	,	PUNCT
ajst-31195	1	42	guowei	guowei	PROPN
ajst-31195	1	43	zhao3	zhao3	PROPN
ajst-31195	1	44	,	,	PUNCT
ajst-31195	1	45	qiqi	qiqi	NOUN
ajst-31195	1	46	liang2	liang2	NOUN
ajst-31195	1	47	,	,	PUNCT
ajst-31195	1	48	bowen	bowen	PROPN
ajst-31195	1	49	li4	li4	VERB
ajst-31195	1	50	1college	1college	PROPN
ajst-31195	1	51	of	of	ADP
ajst-31195	1	52	life	life	NOUN
ajst-31195	1	53	sciences	sciences	PROPN
ajst-31195	1	54	,	,	PUNCT
ajst-31195	1	55	north	north	PROPN
ajst-31195	1	56	china	china	PROPN
ajst-31195	1	57	university	university	PROPN
ajst-31195	1	58	of	of	ADP
ajst-31195	1	59	science	science	NOUN
ajst-31195	1	60	and	and	CCONJ
ajst-31195	1	61	technology	technology	NOUN
ajst-31195	1	62	,	,	PUNCT
ajst-31195	1	63	tangshan	tangshan	ADJ
ajst-31195	1	64	,	,	PUNCT
ajst-31195	1	65	063210	063210	NUM
ajst-31195	1	66	,	,	PUNCT
ajst-31195	1	67	china	china	PROPN
ajst-31195	1	68	2college	2college	PROPN
ajst-31195	1	69	of	of	ADP
ajst-31195	1	70	science	science	PROPN
ajst-31195	1	71	,	,	PUNCT
ajst-31195	1	72	north	north	PROPN
ajst-31195	1	73	china	china	PROPN
ajst-31195	1	74	university	university	PROPN
ajst-31195	1	75	of	of	ADP
ajst-31195	1	76	science	science	NOUN
ajst-31195	1	77	and	and	CCONJ
ajst-31195	1	78	technology	technology	NOUN
ajst-31195	1	79	,	,	PUNCT
ajst-31195	1	80	tangshan	tangshan	ADJ
ajst-31195	1	81	,	,	PUNCT
ajst-31195	1	82	063210	063210	NUM
ajst-31195	1	83	,	,	PUNCT
ajst-31195	1	84	china	china	PROPN
ajst-31195	1	85	3faculty	3faculty	NUM
ajst-31195	1	86	of	of	ADP
ajst-31195	1	87	science	science	NOUN
ajst-31195	1	88	,	,	PUNCT
ajst-31195	1	89	north	north	PROPN
ajst-31195	1	90	china	china	PROPN
ajst-31195	1	91	university	university	PROPN
ajst-31195	1	92	of	of	ADP
ajst-31195	1	93	science	science	NOUN
ajst-31195	1	94	and	and	CCONJ
ajst-31195	1	95	technology	technology	NOUN
ajst-31195	1	96	,	,	PUNCT
ajst-31195	1	97	tangshan	tangshan	ADJ
ajst-31195	1	98	,	,	PUNCT
ajst-31195	1	99	063210	063210	NUM
ajst-31195	1	100	,	,	PUNCT
ajst-31195	1	101	china	china	PROPN
ajst-31195	1	102	4college	4college	PROPN
ajst-31195	1	103	of	of	ADP
ajst-31195	1	104	life	life	NOUN
ajst-31195	1	105	sciences	sciences	PROPN
ajst-31195	1	106	,	,	PUNCT
ajst-31195	1	107	north	north	PROPN
ajst-31195	1	108	china	china	PROPN
ajst-31195	1	109	university	university	PROPN
ajst-31195	1	110	of	of	ADP
ajst-31195	1	111	science	science	NOUN
ajst-31195	1	112	and	and	CCONJ
ajst-31195	1	113	technology	technology	NOUN
ajst-31195	1	114	,	,	PUNCT
ajst-31195	1	115	tangshan	tangshan	ADJ
ajst-31195	1	116	,	,	PUNCT
ajst-31195	1	117	063210	063210	NUM
ajst-31195	1	118	,	,	PUNCT
ajst-31195	1	119	china	china	PROPN
ajst-31195	1	120	abstract	abstract	NOUN
ajst-31195	1	121	:	:	PUNCT
ajst-31195	1	122	to	to	PART
ajst-31195	1	123	address	address	VERB
ajst-31195	1	124	the	the	DET
ajst-31195	1	125	inefficiencies	inefficiency	NOUN
ajst-31195	1	126	and	and	CCONJ
ajst-31195	1	127	high	high	ADJ
ajst-31195	1	128	costs	cost	NOUN
ajst-31195	1	129	inherent	inherent	ADJ
ajst-31195	1	130	in	in	ADP
ajst-31195	1	131	traditional	traditional	ADJ
ajst-31195	1	132	apple	apple	NOUN
ajst-31195	1	133	harvesting	harvesting	NOUN
ajst-31195	1	134	methods	method	NOUN
ajst-31195	1	135	that	that	PRON
ajst-31195	1	136	rely	rely	VERB
ajst-31195	1	137	on	on	ADP
ajst-31195	1	138	manual	manual	ADJ
ajst-31195	1	139	labor	labor	NOUN
ajst-31195	1	140	,	,	PUNCT
ajst-31195	1	141	this	this	DET
ajst-31195	1	142	study	study	NOUN
ajst-31195	1	143	aims	aim	VERB
ajst-31195	1	144	to	to	PART
ajst-31195	1	145	develop	develop	VERB
ajst-31195	1	146	an	an	DET
ajst-31195	1	147	automated	automate	VERB
ajst-31195	1	148	detection	detection	NOUN
ajst-31195	1	149	system	system	NOUN
ajst-31195	1	150	capable	capable	ADJ
ajst-31195	1	151	of	of	ADP
ajst-31195	1	152	accurate	accurate	ADJ
ajst-31195	1	153	apple	apple	NOUN
ajst-31195	1	154	quantity	quantity	NOUN
ajst-31195	1	155	recognition	recognition	NOUN
ajst-31195	1	156	and	and	CCONJ
ajst-31195	1	157	maturity	maturity	NOUN
ajst-31195	1	158	assessment	assessment	NOUN
ajst-31195	1	159	under	under	ADP
ajst-31195	1	160	complex	complex	ADJ
ajst-31195	1	161	orchard	orchard	NOUN
ajst-31195	1	162	conditions	condition	NOUN
ajst-31195	1	163	.	.	PUNCT
ajst-31195	2	1	by	by	ADP
ajst-31195	2	2	integrating	integrate	VERB
ajst-31195	2	3	advanced	advanced	ADJ
ajst-31195	2	4	instance	instance	NOUN
ajst-31195	2	5	segmentation	segmentation	NOUN
ajst-31195	2	6	with	with	ADP
ajst-31195	2	7	agricultural	agricultural	ADJ
ajst-31195	2	8	automation	automation	NOUN
ajst-31195	2	9	technology	technology	NOUN
ajst-31195	2	10	,	,	PUNCT
ajst-31195	2	11	we	we	PRON
ajst-31195	2	12	seek	seek	VERB
ajst-31195	2	13	to	to	PART
ajst-31195	2	14	establish	establish	VERB
ajst-31195	2	15	a	a	DET
ajst-31195	2	16	foundational	foundational	ADJ
ajst-31195	2	17	framework	framework	NOUN
ajst-31195	2	18	for	for	ADP
ajst-31195	2	19	intelligent	intelligent	ADJ
ajst-31195	2	20	harvesting	harvesting	NOUN
ajst-31195	2	21	robots	robot	NOUN
ajst-31195	2	22	.	.	PUNCT
ajst-31195	3	1	the	the	DET
ajst-31195	3	2	proposed	propose	VERB
ajst-31195	3	3	methodology	methodology	NOUN
ajst-31195	3	4	leverages	leverage	VERB
ajst-31195	3	5	the	the	DET
ajst-31195	3	6	yolov8x	yolov8x	PROPN
ajst-31195	3	7	-	-	PUNCT
ajst-31195	3	8	seg	seg	PROPN
ajst-31195	3	9	model	model	NOUN
ajst-31195	3	10	enhanced	enhance	VERB
ajst-31195	3	11	through	through	ADP
ajst-31195	3	12	image	image	NOUN
ajst-31195	3	13	sharpening	sharpening	NOUN
ajst-31195	3	14	and	and	CCONJ
ajst-31195	3	15	median	median	ADJ
ajst-31195	3	16	filtering	filtering	NOUN
ajst-31195	3	17	preprocessing	preprocessing	NOUN
ajst-31195	3	18	,	,	PUNCT
ajst-31195	3	19	which	which	PRON
ajst-31195	3	20	optimizes	optimize	VERB
ajst-31195	3	21	edge	edge	NOUN
ajst-31195	3	22	feature	feature	NOUN
ajst-31195	3	23	extraction	extraction	NOUN
ajst-31195	3	24	while	while	SCONJ
ajst-31195	3	25	suppressing	suppress	VERB
ajst-31195	3	26	environmental	environmental	ADJ
ajst-31195	3	27	noise	noise	NOUN
ajst-31195	3	28	.	.	PUNCT
ajst-31195	4	1	the	the	DET
ajst-31195	4	2	adam	adam	PROPN
ajst-31195	4	3	gradient	gradient	PROPN
ajst-31195	4	4	descent	descent	NOUN
ajst-31195	4	5	algorithm	algorithm	NOUN
ajst-31195	4	6	is	be	AUX
ajst-31195	4	7	systematically	systematically	ADV
ajst-31195	4	8	applied	apply	VERB
ajst-31195	4	9	to	to	PART
ajst-31195	4	10	refine	refine	VERB
ajst-31195	4	11	model	model	NOUN
ajst-31195	4	12	parameters	parameter	NOUN
ajst-31195	4	13	,	,	PUNCT
ajst-31195	4	14	enabling	enable	VERB
ajst-31195	4	15	multi	multi	ADJ
ajst-31195	4	16	-	-	ADJ
ajst-31195	4	17	scale	scale	ADJ
ajst-31195	4	18	feature	feature	NOUN
ajst-31195	4	19	capture	capture	NOUN
ajst-31195	4	20	through	through	ADP
ajst-31195	4	21	convolutional	convolutional	ADJ
ajst-31195	4	22	-	-	PUNCT
ajst-31195	4	23	pooling	pool	VERB
ajst-31195	4	24	layer	layer	NOUN
ajst-31195	4	25	combinations	combination	NOUN
ajst-31195	4	26	and	and	CCONJ
ajst-31195	4	27	precise	precise	ADJ
ajst-31195	4	28	classification	classification	NOUN
ajst-31195	4	29	via	via	ADP
ajst-31195	4	30	fully	fully	ADV
ajst-31195	4	31	connected	connected	ADJ
ajst-31195	4	32	layers	layer	NOUN
ajst-31195	4	33	.	.	PUNCT
ajst-31195	5	1	experimental	experimental	ADJ
ajst-31195	5	2	validations	validation	NOUN
ajst-31195	5	3	demonstrate	demonstrate	VERB
ajst-31195	5	4	that	that	SCONJ
ajst-31195	5	5	our	our	PRON
ajst-31195	5	6	optimized	optimize	VERB
ajst-31195	5	7	framework	framework	NOUN
ajst-31195	5	8	achieves	achieve	VERB
ajst-31195	5	9	a	a	DET
ajst-31195	5	10	12.7	12.7	NUM
ajst-31195	5	11	%	%	NOUN
ajst-31195	5	12	improvement	improvement	NOUN
ajst-31195	5	13	in	in	ADP
ajst-31195	5	14	detection	detection	NOUN
ajst-31195	5	15	accuracy	accuracy	NOUN
ajst-31195	5	16	and	and	CCONJ
ajst-31195	5	17	28.4	28.4	NUM
ajst-31195	5	18	%	%	NOUN
ajst-31195	5	19	faster	fast	ADJ
ajst-31195	5	20	inference	inference	NOUN
ajst-31195	5	21	speed	speed	NOUN
ajst-31195	5	22	compared	compare	VERB
ajst-31195	5	23	to	to	ADP
ajst-31195	5	24	baseline	baseline	NOUN
ajst-31195	5	25	models	model	NOUN
ajst-31195	5	26	,	,	PUNCT
ajst-31195	5	27	effectively	effectively	ADV
ajst-31195	5	28	overcoming	overcome	VERB
ajst-31195	5	29	occlusion	occlusion	NOUN
ajst-31195	5	30	and	and	CCONJ
ajst-31195	5	31	overlapping	overlap	VERB
ajst-31195	5	32	fruit	fruit	NOUN
ajst-31195	5	33	challenges	challenge	NOUN
ajst-31195	5	34	.	.	PUNCT
ajst-31195	6	1	these	these	DET
ajst-31195	6	2	advancements	advancement	NOUN
ajst-31195	6	3	not	not	PART
ajst-31195	6	4	only	only	ADV
ajst-31195	6	5	verify	verify	VERB
ajst-31195	6	6	the	the	DET
ajst-31195	6	7	model	model	NOUN
ajst-31195	6	8	's	's	PART
ajst-31195	6	9	capability	capability	NOUN
ajst-31195	6	10	in	in	ADP
ajst-31195	6	11	maturity	maturity	NOUN
ajst-31195	6	12	differentiation	differentiation	NOUN
ajst-31195	6	13	through	through	ADP
ajst-31195	6	14	spectral	spectral	ADJ
ajst-31195	6	15	analysis	analysis	NOUN
ajst-31195	6	16	but	but	CCONJ
ajst-31195	6	17	also	also	ADV
ajst-31195	6	18	reveal	reveal	VERB
ajst-31195	6	19	its	its	PRON
ajst-31195	6	20	potential	potential	NOUN
ajst-31195	6	21	for	for	ADP
ajst-31195	6	22	real	real	ADJ
ajst-31195	6	23	-	-	PUNCT
ajst-31195	6	24	time	time	NOUN
ajst-31195	6	25	monitoring	monitoring	NOUN
ajst-31195	6	26	applications	application	NOUN
ajst-31195	6	27	.	.	PUNCT
ajst-31195	7	1	the	the	DET
ajst-31195	7	2	research	research	NOUN
ajst-31195	7	3	outcomes	outcome	NOUN
ajst-31195	7	4	provide	provide	VERB
ajst-31195	7	5	critical	critical	ADJ
ajst-31195	7	6	technical	technical	ADJ
ajst-31195	7	7	support	support	NOUN
ajst-31195	7	8	for	for	ADP
ajst-31195	7	9	intelligent	intelligent	ADJ
ajst-31195	7	10	orchard	orchard	NOUN
ajst-31195	7	11	management	management	NOUN
ajst-31195	7	12	systems	system	NOUN
ajst-31195	7	13	,	,	PUNCT
ajst-31195	7	14	marking	mark	VERB
ajst-31195	7	15	a	a	DET
ajst-31195	7	16	significant	significant	ADJ
ajst-31195	7	17	step	step	NOUN
ajst-31195	7	18	toward	toward	ADP
ajst-31195	7	19	reducing	reduce	VERB
ajst-31195	7	20	agricultural	agricultural	ADJ
ajst-31195	7	21	labor	labor	NOUN
ajst-31195	7	22	dependency	dependency	NOUN
ajst-31195	7	23	and	and	CCONJ
ajst-31195	7	24	advancing	advance	VERB
ajst-31195	7	25	precision	precision	NOUN
ajst-31195	7	26	farming	farming	NOUN
ajst-31195	7	27	practices	practice	NOUN
ajst-31195	7	28	.	.	PUNCT
ajst-31195	8	1	keywords	keyword	NOUN
ajst-31195	8	2	:	:	PUNCT
ajst-31195	8	3	image	image	NOUN
ajst-31195	8	4	recognition	recognition	NOUN
ajst-31195	8	5	,	,	PUNCT
ajst-31195	8	6	median	median	ADJ
ajst-31195	8	7	filtering	filtering	NOUN
ajst-31195	8	8	,	,	PUNCT
ajst-31195	8	9	yolov8x	yolov8x	NOUN
ajst-31195	8	10	-	-	PUNCT
ajst-31195	8	11	seg	seg	PROPN
ajst-31195	8	12	,	,	PUNCT
ajst-31195	8	13	adam	adam	PROPN
ajst-31195	8	14	gradient	gradient	PROPN
ajst-31195	8	15	descent	descent	NOUN
ajst-31195	8	16	.	.	PUNCT
ajst-31195	9	1	1	1	X
ajst-31195	9	2	.	.	X
ajst-31195	9	3	introduction	introduction	NOUN
ajst-31195	9	4	as	as	ADP
ajst-31195	9	5	the	the	DET
ajst-31195	9	6	world	world	NOUN
ajst-31195	9	7	's	's	PART
ajst-31195	9	8	largest	large	ADJ
ajst-31195	9	9	producer	producer	NOUN
ajst-31195	9	10	and	and	CCONJ
ajst-31195	9	11	exporter	exporter	NOUN
ajst-31195	9	12	of	of	ADP
ajst-31195	9	13	apples	apple	NOUN
ajst-31195	9	14	,	,	PUNCT
ajst-31195	9	15	china	china	PROPN
ajst-31195	9	16	's	's	PART
ajst-31195	9	17	apple	apple	NOUN
ajst-31195	9	18	industry	industry	NOUN
ajst-31195	9	19	faces	face	VERB
ajst-31195	9	20	critical	critical	ADJ
ajst-31195	9	21	challenges	challenge	NOUN
ajst-31195	9	22	in	in	ADP
ajst-31195	9	23	traditional	traditional	ADJ
ajst-31195	9	24	harvesting	harvesting	NOUN
ajst-31195	9	25	practices	practice	NOUN
ajst-31195	9	26	.	.	PUNCT
ajst-31195	10	1	labor	labor	NOUN
ajst-31195	10	2	shortages	shortage	NOUN
ajst-31195	10	3	have	have	AUX
ajst-31195	10	4	driven	drive	VERB
ajst-31195	10	5	picking	picking	NOUN
ajst-31195	10	6	costs	cost	NOUN
ajst-31195	10	7	to	to	PART
ajst-31195	10	8	exceed	exceed	VERB
ajst-31195	10	9	40	40	NUM
ajst-31195	10	10	%	%	NOUN
ajst-31195	10	11	of	of	ADP
ajst-31195	10	12	total	total	ADJ
ajst-31195	10	13	expenses	expense	NOUN
ajst-31195	10	14	,	,	PUNCT
ajst-31195	10	15	compounded	compound	VERB
ajst-31195	10	16	by	by	ADP
ajst-31195	10	17	safety	safety	NOUN
ajst-31195	10	18	risks	risk	NOUN
ajst-31195	10	19	associated	associate	VERB
ajst-31195	10	20	with	with	ADP
ajst-31195	10	21	elevated	elevated	ADJ
ajst-31195	10	22	work	work	NOUN
ajst-31195	10	23	environments	environment	NOUN
ajst-31195	10	24	.	.	PUNCT
ajst-31195	11	1	while	while	SCONJ
ajst-31195	11	2	machine	machine	NOUN
ajst-31195	11	3	vision	vision	NOUN
ajst-31195	11	4	-	-	PUNCT
ajst-31195	11	5	based	base	VERB
ajst-31195	11	6	robotic	robotic	ADJ
ajst-31195	11	7	systems	system	NOUN
ajst-31195	11	8	offer	offer	VERB
ajst-31195	11	9	a	a	DET
ajst-31195	11	10	promising	promising	ADJ
ajst-31195	11	11	solution	solution	NOUN
ajst-31195	11	12	,	,	PUNCT
ajst-31195	11	13	their	their	PRON
ajst-31195	11	14	effectiveness	effectiveness	NOUN
ajst-31195	11	15	is	be	AUX
ajst-31195	11	16	severely	severely	ADV
ajst-31195	11	17	hampered	hamper	VERB
ajst-31195	11	18	by	by	ADP
ajst-31195	11	19	the	the	DET
ajst-31195	11	20	complexity	complexity	NOUN
ajst-31195	11	21	of	of	ADP
ajst-31195	11	22	orchard	orchard	ADJ
ajst-31195	11	23	scenes	scene	NOUN
ajst-31195	11	24	.	.	PUNCT
ajst-31195	12	1	key	key	ADJ
ajst-31195	12	2	obstacles	obstacle	NOUN
ajst-31195	12	3	include	include	VERB
ajst-31195	12	4	branch	branch	NOUN
ajst-31195	12	5	and	and	CCONJ
ajst-31195	12	6	leaf	leaf	NOUN
ajst-31195	12	7	occlusions	occlusion	NOUN
ajst-31195	12	8	(	(	PUNCT
ajst-31195	12	9	over	over	ADP
ajst-31195	12	10	30	30	NUM
ajst-31195	12	11	%	%	NOUN
ajst-31195	12	12	blind	blind	ADJ
ajst-31195	12	13	spots	spot	NOUN
ajst-31195	12	14	)	)	PUNCT
ajst-31195	12	15	,	,	PUNCT
ajst-31195	12	16	minimal	minimal	ADJ
ajst-31195	12	17	color	color	NOUN
ajst-31195	12	18	contrast	contrast	NOUN
ajst-31195	12	19	between	between	ADP
ajst-31195	12	20	apples	apple	NOUN
ajst-31195	12	21	and	and	CCONJ
ajst-31195	12	22	backgrounds	background	NOUN
ajst-31195	12	23	(	(	PUNCT
ajst-31195	12	24	merely	merely	ADV
ajst-31195	12	25	15	15	NUM
ajst-31195	12	26	%	%	NOUN
ajst-31195	12	27	hsv	hsv	NOUN
ajst-31195	12	28	difference	difference	NOUN
ajst-31195	12	29	)	)	PUNCT
ajst-31195	12	30	,	,	PUNCT
ajst-31195	12	31	and	and	CCONJ
ajst-31195	12	32	dynamic	dynamic	ADJ
ajst-31195	12	33	lighting	lighting	NOUN
ajst-31195	12	34	variations	variation	NOUN
ajst-31195	12	35	,	,	PUNCT
ajst-31195	12	36	which	which	PRON
ajst-31195	12	37	collectively	collectively	ADV
ajst-31195	12	38	degrade	degrade	VERB
ajst-31195	12	39	traditional	traditional	ADJ
ajst-31195	12	40	image	image	NOUN
ajst-31195	12	41	processing	processing	NOUN
ajst-31195	12	42	methods	method	NOUN
ajst-31195	12	43	to	to	ADP
ajst-31195	12	44	below	below	ADP
ajst-31195	12	45	80	80	NUM
ajst-31195	12	46	%	%	NOUN
ajst-31195	12	47	accuracy	accuracy	NOUN
ajst-31195	13	1	[	[	X
ajst-31195	13	2	1	1	NUM
ajst-31195	13	3	]	]	PUNCT
ajst-31195	13	4	.	.	PUNCT
ajst-31195	14	1	further	further	ADJ
ajst-31195	14	2	complications	complication	NOUN
ajst-31195	14	3	arise	arise	VERB
ajst-31195	14	4	from	from	ADP
ajst-31195	14	5	adverse	adverse	ADJ
ajst-31195	14	6	weather	weather	NOUN
ajst-31195	14	7	conditions	condition	NOUN
ajst-31195	14	8	(	(	PUNCT
ajst-31195	14	9	e.g.	e.g.	ADV
ajst-31195	14	10	,	,	PUNCT
ajst-31195	14	11	rain	rain	NOUN
ajst-31195	14	12	,	,	PUNCT
ajst-31195	14	13	haze	haze	NOUN
ajst-31195	14	14	)	)	PUNCT
ajst-31195	14	15	,	,	PUNCT
ajst-31195	14	16	reflective	reflective	ADJ
ajst-31195	14	17	bag	bag	NOUN
ajst-31195	14	18	interference	interference	NOUN
ajst-31195	14	19	,	,	PUNCT
ajst-31195	14	20	and	and	CCONJ
ajst-31195	14	21	significant	significant	ADJ
ajst-31195	14	22	fruit	fruit	NOUN
ajst-31195	14	23	size	size	NOUN
ajst-31195	14	24	variations	variation	NOUN
ajst-31195	14	25	(	(	PUNCT
ajst-31195	14	26	40–90	40–90	NUM
ajst-31195	14	27	mm	mm	NOUN
ajst-31195	14	28	diameter	diameter	NOUN
ajst-31195	14	29	)	)	PUNCT
ajst-31195	14	30	,	,	PUNCT
ajst-31195	14	31	rendering	render	VERB
ajst-31195	14	32	existing	exist	VERB
ajst-31195	14	33	algorithms	algorithm	NOUN
ajst-31195	14	34	unstable	unstable	ADJ
ajst-31195	14	35	in	in	ADP
ajst-31195	14	36	dynamic	dynamic	ADJ
ajst-31195	14	37	environments	environment	NOUN
ajst-31195	14	38	.	.	PUNCT
ajst-31195	15	1	to	to	PART
ajst-31195	15	2	address	address	VERB
ajst-31195	15	3	these	these	DET
ajst-31195	15	4	challenges	challenge	NOUN
ajst-31195	15	5	,	,	PUNCT
ajst-31195	15	6	advancing	advance	VERB
ajst-31195	15	7	deep	deep	ADJ
ajst-31195	15	8	learning	learning	NOUN
ajst-31195	15	9	algorithms	algorithm	NOUN
ajst-31195	15	10	with	with	ADP
ajst-31195	15	11	multispectral	multispectral	ADJ
ajst-31195	15	12	imaging	imaging	NOUN
ajst-31195	15	13	and	and	CCONJ
ajst-31195	15	14	3d	3d	NUM
ajst-31195	15	15	reconstruction	reconstruction	NOUN
ajst-31195	15	16	,	,	PUNCT
ajst-31195	15	17	while	while	SCONJ
ajst-31195	15	18	establishing	establish	VERB
ajst-31195	15	19	standardized	standardized	ADJ
ajst-31195	15	20	multi	multi	ADJ
ajst-31195	15	21	-	-	ADJ
ajst-31195	15	22	region	region	ADJ
ajst-31195	15	23	image	image	NOUN
ajst-31195	15	24	databases	database	NOUN
ajst-31195	15	25	,	,	PUNCT
ajst-31195	15	26	is	be	AUX
ajst-31195	15	27	imperative	imperative	ADJ
ajst-31195	15	28	.	.	PUNCT
ajst-31195	16	1	equally	equally	ADV
ajst-31195	16	2	critical	critical	ADJ
ajst-31195	16	3	is	be	AUX
ajst-31195	16	4	developing	develop	VERB
ajst-31195	16	5	lightweight	lightweight	ADJ
ajst-31195	16	6	models	model	NOUN
ajst-31195	16	7	that	that	PRON
ajst-31195	16	8	balance	balance	VERB
ajst-31195	16	9	>	>	NOUN
ajst-31195	16	10	95	95	NUM
ajst-31195	16	11	%	%	NOUN
ajst-31195	16	12	recognition	recognition	NOUN
ajst-31195	16	13	accuracy	accuracy	NOUN
ajst-31195	16	14	with	with	ADP
ajst-31195	16	15	real	real	ADJ
ajst-31195	16	16	-	-	PUNCT
ajst-31195	16	17	time	time	NOUN
ajst-31195	16	18	processing	processing	NOUN
ajst-31195	16	19	capabilities	capability	NOUN
ajst-31195	16	20	,	,	PUNCT
ajst-31195	16	21	thereby	thereby	ADV
ajst-31195	16	22	accelerating	accelerate	VERB
ajst-31195	16	23	the	the	DET
ajst-31195	16	24	adoption	adoption	NOUN
ajst-31195	16	25	of	of	ADP
ajst-31195	16	26	automated	automate	VERB
ajst-31195	16	27	harvesting	harvesting	NOUN
ajst-31195	16	28	systems	system	NOUN
ajst-31195	16	29	.	.	PUNCT
ajst-31195	17	1	existing	exist	VERB
ajst-31195	17	2	studies	study	NOUN
ajst-31195	17	3	on	on	ADP
ajst-31195	17	4	apple	apple	NOUN
ajst-31195	17	5	detection	detection	NOUN
ajst-31195	17	6	predominantly	predominantly	ADV
ajst-31195	17	7	rely	rely	VERB
ajst-31195	17	8	on	on	ADP
ajst-31195	17	9	deep	deep	ADJ
ajst-31195	17	10	learning	learning	NOUN
ajst-31195	17	11	frameworks	framework	NOUN
ajst-31195	17	12	,	,	PUNCT
ajst-31195	17	13	yet	yet	CCONJ
ajst-31195	17	14	fundamental	fundamental	ADJ
ajst-31195	17	15	limitations	limitation	NOUN
ajst-31195	17	16	persist	persist	VERB
ajst-31195	17	17	.	.	PUNCT
ajst-31195	18	1	for	for	ADP
ajst-31195	18	2	instance	instance	NOUN
ajst-31195	18	3	,	,	PUNCT
ajst-31195	18	4	lightweight	lightweight	ADJ
ajst-31195	18	5	segmentation	segmentation	NOUN
ajst-31195	18	6	networks	network	NOUN
ajst-31195	18	7	like	like	ADP
ajst-31195	18	8	fastscnn	fastscnn	NOUN
ajst-31195	18	9	prioritize	prioritize	VERB
ajst-31195	18	10	computational	computational	ADJ
ajst-31195	18	11	efficiency	efficiency	NOUN
ajst-31195	18	12	through	through	ADP
ajst-31195	18	13	multiscale	multiscale	ADJ
ajst-31195	18	14	feature	feature	NOUN
ajst-31195	18	15	fusion	fusion	NOUN
ajst-31195	18	16	[	[	X
ajst-31195	18	17	2	2	NUM
ajst-31195	18	18	]	]	PUNCT
ajst-31195	18	19	.	.	PUNCT
ajst-31195	19	1	however	however	ADV
ajst-31195	19	2	,	,	PUNCT
ajst-31195	19	3	their	their	PRON
ajst-31195	19	4	fixed	fix	VERB
ajst-31195	19	5	receptive	receptive	ADJ
ajst-31195	19	6	fields	field	NOUN
ajst-31195	19	7	and	and	CCONJ
ajst-31195	19	8	hierarchical	hierarchical	ADJ
ajst-31195	19	9	context	context	NOUN
ajst-31195	19	10	extraction	extraction	NOUN
ajst-31195	19	11	mechanisms	mechanism	NOUN
ajst-31195	19	12	struggle	struggle	VERB
ajst-31195	19	13	to	to	PART
ajst-31195	19	14	adapt	adapt	VERB
ajst-31195	19	15	to	to	PART
ajst-31195	19	16	diverse	diverse	VERB
ajst-31195	19	17	apple	apple	NOUN
ajst-31195	19	18	sizes	size	NOUN
ajst-31195	19	19	and	and	CCONJ
ajst-31195	19	20	cluttered	cluttered	ADJ
ajst-31195	19	21	backgrounds	background	NOUN
ajst-31195	19	22	,	,	PUNCT
ajst-31195	19	23	resulting	result	VERB
ajst-31195	19	24	in	in	ADP
ajst-31195	19	25	poor	poor	ADJ
ajst-31195	19	26	robustness	robustness	NOUN
ajst-31195	19	27	.	.	PUNCT
ajst-31195	20	1	this	this	DET
ajst-31195	20	2	structural	structural	ADJ
ajst-31195	20	3	rigidity	rigidity	NOUN
ajst-31195	20	4	,	,	PUNCT
ajst-31195	20	5	coupled	couple	VERB
ajst-31195	20	6	with	with	ADP
ajst-31195	20	7	inadequate	inadequate	ADJ
ajst-31195	20	8	noise	noise	NOUN
ajst-31195	20	9	suppression	suppression	NOUN
ajst-31195	20	10	strategies	strategy	NOUN
ajst-31195	20	11	,	,	PUNCT
ajst-31195	20	12	exacerbates	exacerbate	VERB
ajst-31195	20	13	misdetections	misdetection	NOUN
ajst-31195	20	14	under	under	ADP
ajst-31195	20	15	hazy	hazy	ADJ
ajst-31195	20	16	or	or	CCONJ
ajst-31195	20	17	rainy	rainy	ADJ
ajst-31195	20	18	conditions	condition	NOUN
ajst-31195	20	19	.	.	PUNCT
ajst-31195	21	1	meanwhile	meanwhile	ADV
ajst-31195	21	2	,	,	PUNCT
ajst-31195	21	3	occlusion	occlusion	NOUN
ajst-31195	21	4	-	-	PUNCT
ajst-31195	21	5	optimized	optimize	VERB
ajst-31195	21	6	approaches	approach	NOUN
ajst-31195	21	7	such	such	ADJ
ajst-31195	21	8	as	as	ADP
ajst-31195	21	9	mask	mask	NOUN
ajst-31195	21	10	r	r	NOUN
ajst-31195	21	11	-	-	PUNCT
ajst-31195	21	12	cnn	cnn	PROPN
ajst-31195	21	13	variants	variant	NOUN
ajst-31195	21	14	achieve	achieve	VERB
ajst-31195	21	15	82.3	82.3	NUM
ajst-31195	21	16	%	%	NOUN
ajst-31195	21	17	accuracy	accuracy	NOUN
ajst-31195	21	18	in	in	ADP
ajst-31195	21	19	occlusion	occlusion	NOUN
ajst-31195	21	20	scenarios	scenario	NOUN
ajst-31195	21	21	but	but	CCONJ
ajst-31195	21	22	suffer	suffer	VERB
ajst-31195	21	23	from	from	ADP
ajst-31195	21	24	computationally	computationally	ADV
ajst-31195	21	25	intensive	intensive	ADJ
ajst-31195	21	26	two	two	NUM
ajst-31195	21	27	-	-	PUNCT
ajst-31195	21	28	stage	stage	NOUN
ajst-31195	21	29	architectures	architecture	NOUN
ajst-31195	21	30	,	,	PUNCT
ajst-31195	21	31	limiting	limit	VERB
ajst-31195	21	32	inference	inference	NOUN
ajst-31195	21	33	speeds	speed	NOUN
ajst-31195	21	34	to	to	ADP
ajst-31195	21	35	15	15	NUM
ajst-31195	21	36	fps	fps	NOUN
ajst-31195	21	37	—	—	PUNCT
ajst-31195	21	38	insufficient	insufficient	ADJ
ajst-31195	21	39	for	for	ADP
ajst-31195	21	40	realtime	realtime	NOUN
ajst-31195	21	41	orchard	orchard	NOUN
ajst-31195	21	42	operations	operation	NOUN
ajst-31195	21	43	[	[	X
ajst-31195	21	44	2,3	2,3	NUM
ajst-31195	21	45	]	]	PUNCT
ajst-31195	21	46	.	.	PUNCT
ajst-31195	22	1	these	these	DET
ajst-31195	22	2	shortcomings	shortcoming	NOUN
ajst-31195	22	3	stem	stem	VERB
ajst-31195	22	4	from	from	ADP
ajst-31195	22	5	three	three	NUM
ajst-31195	22	6	core	core	NOUN
ajst-31195	22	7	issues	issue	NOUN
ajst-31195	22	8	:	:	PUNCT
ajst-31195	22	9	(	(	PUNCT
ajst-31195	22	10	1	1	X
ajst-31195	22	11	)	)	PUNCT
ajst-31195	22	12	inflexible	inflexible	ADJ
ajst-31195	22	13	feature	feature	NOUN
ajst-31195	22	14	learning	learn	VERB
ajst-31195	22	15	due	due	ADP
ajst-31195	22	16	to	to	ADP
ajst-31195	22	17	static	static	ADJ
ajst-31195	22	18	convolutional	convolutional	ADJ
ajst-31195	22	19	layers	layer	NOUN
ajst-31195	22	20	,	,	PUNCT
ajst-31195	22	21	which	which	PRON
ajst-31195	22	22	fail	fail	VERB
ajst-31195	22	23	to	to	PART
ajst-31195	22	24	capture	capture	VERB
ajst-31195	22	25	multi	multi	ADJ
ajst-31195	22	26	-	-	ADJ
ajst-31195	22	27	scale	scale	ADJ
ajst-31195	22	28	spatial	spatial	ADJ
ajst-31195	22	29	relationships	relationship	NOUN
ajst-31195	22	30	;	;	PUNCT
ajst-31195	22	31	(	(	PUNCT
ajst-31195	22	32	2	2	X
ajst-31195	22	33	)	)	PUNCT
ajst-31195	22	34	noise	noise	NOUN
ajst-31195	22	35	amplification	amplification	NOUN
ajst-31195	22	36	from	from	ADP
ajst-31195	22	37	insufficient	insufficient	ADJ
ajst-31195	22	38	preprocessing	preprocessing	NOUN
ajst-31195	22	39	,	,	PUNCT
ajst-31195	22	40	particularly	particularly	ADV
ajst-31195	22	41	under	under	ADP
ajst-31195	22	42	environmental	environmental	ADJ
ajst-31195	22	43	interference	interference	NOUN
ajst-31195	22	44	;	;	PUNCT
ajst-31195	22	45	and	and	CCONJ
ajst-31195	22	46	(	(	PUNCT
ajst-31195	22	47	3	3	X
ajst-31195	22	48	)	)	PUNCT
ajst-31195	22	49	error	error	NOUN
ajst-31195	22	50	accumulation	accumulation	NOUN
ajst-31195	22	51	in	in	ADP
ajst-31195	22	52	dense	dense	ADJ
ajst-31195	22	53	target	target	NOUN
ajst-31195	22	54	localization	localization	NOUN
ajst-31195	22	55	caused	cause	VERB
ajst-31195	22	56	by	by	ADP
ajst-31195	22	57	overlapping	overlap	VERB
ajst-31195	22	58	apples	apple	NOUN
ajst-31195	22	59	and	and	CCONJ
ajst-31195	22	60	weak	weak	ADJ
ajst-31195	22	61	feature	feature	NOUN
ajst-31195	22	62	discrimination	discrimination	NOUN
ajst-31195	22	63	.	.	PUNCT
ajst-31195	23	1	to	to	PART
ajst-31195	23	2	overcome	overcome	VERB
ajst-31195	23	3	these	these	DET
ajst-31195	23	4	limitations	limitation	NOUN
ajst-31195	23	5	,	,	PUNCT
ajst-31195	23	6	this	this	DET
ajst-31195	23	7	study	study	NOUN
ajst-31195	23	8	proposes	propose	VERB
ajst-31195	23	9	an	an	DET
ajst-31195	23	10	enhanced	enhanced	ADJ
ajst-31195	23	11	yolov8x	yolov8x	NOUN
ajst-31195	23	12	-	-	PUNCT
ajst-31195	23	13	seg	seg	PROPN
ajst-31195	23	14	framework	framework	NOUN
ajst-31195	23	15	with	with	ADP
ajst-31195	23	16	three	three	NUM
ajst-31195	23	17	key	key	ADJ
ajst-31195	23	18	innovations	innovation	NOUN
ajst-31195	23	19	.	.	PUNCT
ajst-31195	24	1	first	first	ADV
ajst-31195	24	2	,	,	PUNCT
ajst-31195	24	3	a	a	DET
ajst-31195	24	4	hybrid	hybrid	ADJ
ajst-31195	24	5	preprocessing	preprocessing	NOUN
ajst-31195	24	6	module	module	NOUN
ajst-31195	24	7	integrates	integrate	NOUN
ajst-31195	24	8	sharpening	sharpen	VERB
ajst-31195	24	9	and	and	CCONJ
ajst-31195	24	10	median	median	ADJ
ajst-31195	24	11	filtering	filtering	NOUN
ajst-31195	24	12	to	to	PART
ajst-31195	24	13	amplify	amplify	VERB
ajst-31195	24	14	edge	edge	NOUN
ajst-31195	24	15	features	feature	NOUN
ajst-31195	24	16	while	while	SCONJ
ajst-31195	24	17	suppressing	suppress	VERB
ajst-31195	24	18	noise	noise	NOUN
ajst-31195	24	19	(	(	PUNCT
ajst-31195	24	20	35	35	NUM
ajst-31195	24	21	%	%	NOUN
ajst-31195	24	22	reduction	reduction	NOUN
ajst-31195	24	23	in	in	ADP
ajst-31195	24	24	interference	interference	NOUN
ajst-31195	24	25	)	)	PUNCT
ajst-31195	24	26	.	.	PUNCT
ajst-31195	25	1	second	second	ADJ
ajst-31195	25	2	,	,	PUNCT
ajst-31195	25	3	dynamic	dynamic	ADJ
ajst-31195	25	4	multi	multi	ADJ
ajst-31195	25	5	-	-	ADJ
ajst-31195	25	6	scale	scale	ADJ
ajst-31195	25	7	feature	feature	NOUN
ajst-31195	25	8	fusion	fusion	NOUN
ajst-31195	25	9	is	be	AUX
ajst-31195	25	10	achieved	achieve	VERB
ajst-31195	25	11	through	through	ADP
ajst-31195	25	12	optimized	optimize	VERB
ajst-31195	25	13	pooling	pooling	NOUN
ajst-31195	25	14	strategies	strategy	NOUN
ajst-31195	25	15	and	and	CCONJ
ajst-31195	25	16	adam	adam	NOUN
ajst-31195	25	17	-	-	PUNCT
ajst-31195	25	18	driven	drive	VERB
ajst-31195	25	19	parameter	parameter	NOUN
ajst-31195	25	20	tuning	tuning	NOUN
ajst-31195	25	21	(	(	PUNCT
ajst-31195	25	22	learning	learn	VERB
ajst-31195	25	23	rate	rate	NOUN
ajst-31195	25	24	:	:	PUNCT
ajst-31195	25	25	0.001	0.001	NUM
ajst-31195	25	26	,	,	PUNCT
ajst-31195	25	27	l2	l2	NOUN
ajst-31195	25	28	regularization	regularization	NOUN
ajst-31195	25	29	:	:	PUNCT
ajst-31195	25	30	0.0001	0.0001	NUM
ajst-31195	25	31	)	)	PUNCT
ajst-31195	25	32	,	,	PUNCT
ajst-31195	25	33	enabling	enable	VERB
ajst-31195	25	34	adaptive	adaptive	ADJ
ajst-31195	25	35	learning	learning	NOUN
ajst-31195	25	36	across	across	ADP
ajst-31195	25	37	varying	vary	VERB
ajst-31195	25	38	apple	apple	NOUN
ajst-31195	25	39	sizes	size	NOUN
ajst-31195	25	40	.	.	PUNCT
ajst-31195	26	1	third	third	ADJ
ajst-31195	26	2	,	,	PUNCT
ajst-31195	26	3	a	a	DET
ajst-31195	26	4	lightweight	lightweight	ADJ
ajst-31195	26	5	instance	instance	NOUN
ajst-31195	26	6	segmentation	segmentation	NOUN
ajst-31195	26	7	head	head	NOUN
ajst-31195	26	8	reduces	reduce	VERB
ajst-31195	26	9	computational	computational	ADJ
ajst-31195	26	10	overhead	overhead	NOUN
ajst-31195	26	11	by	by	ADP
ajst-31195	26	12	40	40	NUM
ajst-31195	26	13	%	%	NOUN
ajst-31195	26	14	compared	compare	VERB
ajst-31195	26	15	to	to	ADP
ajst-31195	26	16	conventional	conventional	ADJ
ajst-31195	26	17	yolo	yolo	ADJ
ajst-31195	26	18	architectures	architecture	NOUN
ajst-31195	26	19	,	,	PUNCT
ajst-31195	26	20	addressing	address	VERB
ajst-31195	26	21	real	real	ADJ
ajst-31195	26	22	-	-	PUNCT
ajst-31195	26	23	time	time	NOUN
ajst-31195	26	24	constraints	constraint	NOUN
ajst-31195	26	25	without	without	ADP
ajst-31195	26	26	sacrificing	sacrifice	VERB
ajst-31195	26	27	accuracy	accuracy	NOUN
ajst-31195	26	28	.	.	PUNCT
ajst-31195	27	1	experimental	experimental	ADJ
ajst-31195	27	2	validation	validation	NOUN
ajst-31195	27	3	on	on	ADP
ajst-31195	27	4	200	200	NUM
ajst-31195	27	5	real	real	ADJ
ajst-31195	27	6	orchard	orchard	NOUN
ajst-31195	27	7	images	image	NOUN
ajst-31195	27	8	demonstrates	demonstrate	VERB
ajst-31195	27	9	95.2	95.2	NUM
ajst-31195	27	10	%	%	NOUN
ajst-31195	27	11	detection	detection	NOUN
ajst-31195	27	12	accuracy	accuracy	NOUN
ajst-31195	27	13	(	(	PUNCT
ajst-31195	27	14	15	15	NUM
ajst-31195	27	15	%	%	NOUN
ajst-31195	27	16	higher	high	ADJ
ajst-31195	27	17	than	than	ADP
ajst-31195	27	18	traditional	traditional	ADJ
ajst-31195	27	19	methods	method	NOUN
ajst-31195	27	20	)	)	PUNCT
ajst-31195	27	21	and	and	CCONJ
ajst-31195	27	22	28	28	NUM
ajst-31195	27	23	fps	fps	PROPN
ajst-31195	27	24	inference	inference	NOUN
ajst-31195	27	25	speed	speed	NOUN
ajst-31195	27	26	on	on	ADP
ajst-31195	27	27	embedded	embed	VERB
ajst-31195	27	28	hardware	hardware	NOUN
ajst-31195	27	29	,	,	PUNCT
ajst-31195	27	30	effectively	effectively	ADV
ajst-31195	27	31	resolving	resolve	VERB
ajst-31195	27	32	challenges	challenge	NOUN
ajst-31195	27	33	in	in	ADP
ajst-31195	27	34	dense	dense	ADJ
ajst-31195	27	35	target	target	NOUN
ajst-31195	27	36	counting	counting	NOUN
ajst-31195	27	37	,	,	PUNCT
ajst-31195	27	38	occlusion	occlusion	NOUN
ajst-31195	27	39	handling	handling	NOUN
ajst-31195	27	40	,	,	PUNCT
ajst-31195	27	41	and	and	CCONJ
ajst-31195	27	42	environmental	environmental	ADJ
ajst-31195	27	43	adaptability	adaptability	NOUN
ajst-31195	27	44	.	.	PUNCT
ajst-31195	28	1	this	this	DET
ajst-31195	28	2	work	work	NOUN
ajst-31195	28	3	bridges	bridge	VERB
ajst-31195	28	4	the	the	DET
ajst-31195	28	5	gap	gap	NOUN
ajst-31195	28	6	between	between	ADP
ajst-31195	28	7	theoretical	theoretical	ADJ
ajst-31195	28	8	research	research	NOUN
ajst-31195	28	9	and	and	CCONJ
ajst-31195	28	10	practical	practical	ADJ
ajst-31195	28	11	deployment	deployment	NOUN
ajst-31195	28	12	,	,	PUNCT
ajst-31195	28	13	offering	offer	VERB
ajst-31195	28	14	a	a	DET
ajst-31195	28	15	robust	robust	ADJ
ajst-31195	28	16	foundation	foundation	NOUN
ajst-31195	28	17	for	for	ADP
ajst-31195	28	18	intelligent	intelligent	ADJ
ajst-31195	28	19	apple	apple	NOUN
ajst-31195	28	20	harvesting	harvesting	NOUN
ajst-31195	28	21	systems	system	NOUN
ajst-31195	28	22	.	.	PUNCT
ajst-31195	29	1	18	18	NUM
ajst-31195	29	2	2	2	NUM
ajst-31195	29	3	.	.	PUNCT
ajst-31195	29	4	research	research	NOUN
ajst-31195	29	5	method	method	NOUN
ajst-31195	29	6	2.1	2.1	NUM
ajst-31195	29	7	.	.	PUNCT
ajst-31195	30	1	data	datum	NOUN
ajst-31195	30	2	collection	collection	NOUN
ajst-31195	30	3	in	in	ADP
ajst-31195	30	4	this	this	DET
ajst-31195	30	5	study	study	NOUN
ajst-31195	30	6	,	,	PUNCT
ajst-31195	30	7	200	200	NUM
ajst-31195	30	8	images	image	NOUN
ajst-31195	30	9	of	of	ADP
ajst-31195	30	10	harvested	harvest	VERB
ajst-31195	30	11	apples	apple	NOUN
ajst-31195	30	12	were	be	AUX
ajst-31195	30	13	collected	collect	VERB
ajst-31195	30	14	by	by	ADP
ajst-31195	30	15	ourselves	ourselves	PRON
ajst-31195	30	16	.	.	PUNCT
ajst-31195	31	1	the	the	DET
ajst-31195	31	2	collection	collection	NOUN
ajst-31195	31	3	site	site	NOUN
ajst-31195	31	4	was	be	AUX
ajst-31195	31	5	shengguoyuan	shengguoyuan	ADJ
ajst-31195	31	6	ecological	ecological	ADJ
ajst-31195	31	7	orchard	orchard	NOUN
ajst-31195	31	8	in	in	ADP
ajst-31195	31	9	sujiadian	sujiadian	PROPN
ajst-31195	31	10	town	town	NOUN
ajst-31195	31	11	,	,	PUNCT
ajst-31195	31	12	qixia	qixia	PROPN
ajst-31195	31	13	city	city	PROPN
ajst-31195	31	14	,	,	PUNCT
ajst-31195	31	15	yantai	yantai	PROPN
ajst-31195	31	16	city	city	PROPN
ajst-31195	31	17	,	,	PUNCT
ajst-31195	31	18	shandong	shandong	PROPN
ajst-31195	31	19	province	province	PROPN
ajst-31195	31	20	,	,	PUNCT
ajst-31195	31	21	and	and	CCONJ
ajst-31195	31	22	the	the	DET
ajst-31195	31	23	images	image	NOUN
ajst-31195	31	24	were	be	AUX
ajst-31195	31	25	captured	capture	VERB
ajst-31195	31	26	with	with	ADP
ajst-31195	31	27	a	a	DET
ajst-31195	31	28	nikon	nikon	ADJ
ajst-31195	31	29	d850	d850	PROPN
ajst-31195	31	30	digital	digital	ADJ
ajst-31195	31	31	single	single	ADJ
ajst-31195	31	32	-	-	PUNCT
ajst-31195	31	33	lens	lens	NOUN
ajst-31195	31	34	reflex	reflex	NOUN
ajst-31195	31	35	(	(	PUNCT
ajst-31195	31	36	dslr	dslr	NOUN
ajst-31195	31	37	)	)	PUNCT
ajst-31195	31	38	camera	camera	NOUN
ajst-31195	31	39	in	in	ADP
ajst-31195	31	40	a	a	DET
ajst-31195	31	41	natural	natural	ADJ
ajst-31195	31	42	environment	environment	NOUN
ajst-31195	31	43	during	during	ADP
ajst-31195	31	44	the	the	DET
ajst-31195	31	45	apple	apple	NOUN
ajst-31195	31	46	harvest	harvest	NOUN
ajst-31195	31	47	season	season	NOUN
ajst-31195	31	48	for	for	ADP
ajst-31195	31	49	image	image	NOUN
ajst-31195	31	50	preprocessing	preprocessing	NOUN
ajst-31195	31	51	,	,	PUNCT
ajst-31195	31	52	feature	feature	NOUN
ajst-31195	31	53	extraction	extraction	NOUN
ajst-31195	31	54	,	,	PUNCT
ajst-31195	31	55	and	and	CCONJ
ajst-31195	31	56	apple	apple	NOUN
ajst-31195	31	57	counting	counting	NOUN
ajst-31195	31	58	.	.	PUNCT
ajst-31195	32	1	these	these	DET
ajst-31195	32	2	images	image	NOUN
ajst-31195	32	3	were	be	AUX
ajst-31195	32	4	taken	take	VERB
ajst-31195	32	5	in	in	ADP
ajst-31195	32	6	natural	natural	ADJ
ajst-31195	32	7	environment	environment	NOUN
ajst-31195	32	8	,	,	PUNCT
ajst-31195	32	9	covering	cover	VERB
ajst-31195	32	10	different	different	ADJ
ajst-31195	32	11	angles	angle	NOUN
ajst-31195	32	12	and	and	CCONJ
ajst-31195	32	13	lighting	lighting	NOUN
ajst-31195	32	14	conditions	condition	NOUN
ajst-31195	32	15	,	,	PUNCT
ajst-31195	32	16	which	which	PRON
ajst-31195	32	17	can	can	AUX
ajst-31195	32	18	reflect	reflect	VERB
ajst-31195	32	19	the	the	DET
ajst-31195	32	20	real	real	ADJ
ajst-31195	32	21	state	state	NOUN
ajst-31195	32	22	of	of	ADP
ajst-31195	32	23	apples	apple	NOUN
ajst-31195	32	24	in	in	ADP
ajst-31195	32	25	the	the	DET
ajst-31195	32	26	orchard	orchard	NOUN
ajst-31195	32	27	.	.	PUNCT
ajst-31195	33	1	the	the	DET
ajst-31195	33	2	images	image	NOUN
ajst-31195	33	3	were	be	AUX
ajst-31195	33	4	processed	process	VERB
ajst-31195	33	5	by	by	ADP
ajst-31195	33	6	the	the	DET
ajst-31195	33	7	yolov8x	yolov8x	PROPN
ajst-31195	33	8	-	-	PUNCT
ajst-31195	33	9	seg	seg	PROPN
ajst-31195	33	10	model	model	NOUN
ajst-31195	33	11	,	,	PUNCT
ajst-31195	33	12	which	which	PRON
ajst-31195	33	13	was	be	AUX
ajst-31195	33	14	pre	pre	VERB
ajst-31195	33	15	-	-	VERB
ajst-31195	33	16	trained	train	VERB
ajst-31195	33	17	on	on	ADP
ajst-31195	33	18	the	the	DET
ajst-31195	33	19	coco	coco	PROPN
ajst-31195	33	20	(	(	PUNCT
ajst-31195	33	21	https://image-net.org/	https://image-net.org/	NOUN
ajst-31195	33	22	)	)	PUNCT
ajst-31195	33	23	detection	detection	NOUN
ajst-31195	33	24	dataset	dataset	NOUN
ajst-31195	33	25	(	(	PUNCT
ajst-31195	33	26	image	image	NOUN
ajst-31195	33	27	resolution	resolution	NOUN
ajst-31195	33	28	of	of	ADP
ajst-31195	33	29	640	640	NUM
ajst-31195	33	30	)	)	PUNCT
ajst-31195	33	31	,	,	PUNCT
ajst-31195	33	32	coco	coco	PROPN
ajst-31195	33	33	segmentation	segmentation	PROPN
ajst-31195	33	34	dataset	dataset	NOUN
ajst-31195	33	35	(	(	PUNCT
ajst-31195	33	36	image	image	NOUN
ajst-31195	33	37	resolution	resolution	NOUN
ajst-31195	33	38	of	of	ADP
ajst-31195	33	39	640	640	NUM
ajst-31195	33	40	)	)	PUNCT
ajst-31195	33	41	and	and	CCONJ
ajst-31195	33	42	imagenet	imagenet	NOUN
ajst-31195	33	43	(	(	PUNCT
ajst-31195	33	44	https://image-net.org/	https://image-net.org/	NOUN
ajst-31195	33	45	)	)	PUNCT
ajst-31195	33	46	dataset	dataset	NOUN
ajst-31195	33	47	(	(	PUNCT
ajst-31195	33	48	image	image	NOUN
ajst-31195	33	49	resolution	resolution	NOUN
ajst-31195	33	50	of	of	ADP
ajst-31195	33	51	224	224	NUM
ajst-31195	33	52	)	)	PUNCT
ajst-31195	33	53	.	.	PUNCT
ajst-31195	34	1	the	the	DET
ajst-31195	34	2	model	model	NOUN
ajst-31195	34	3	is	be	AUX
ajst-31195	34	4	pre	pre	ADJ
ajst-31195	34	5	-	-	VERB
ajst-31195	34	6	trained	train	VERB
ajst-31195	34	7	on	on	ADP
ajst-31195	34	8	coco	coco	PROPN
ajst-31195	34	9	detection	detection	PROPN
ajst-31195	34	10	dataset	dataset	NOUN
ajst-31195	34	11	(	(	PUNCT
ajst-31195	34	12	image	image	NOUN
ajst-31195	34	13	resolution	resolution	NOUN
ajst-31195	34	14	of	of	ADP
ajst-31195	34	15	640	640	NUM
ajst-31195	34	16	)	)	PUNCT
ajst-31195	34	17	,	,	PUNCT
ajst-31195	34	18	coco	coco	PROPN
ajst-31195	34	19	segmentation	segmentation	PROPN
ajst-31195	34	20	dataset	dataset	NOUN
ajst-31195	34	21	(	(	PUNCT
ajst-31195	34	22	image	image	NOUN
ajst-31195	34	23	resolution	resolution	NOUN
ajst-31195	34	24	of	of	ADP
ajst-31195	34	25	640	640	NUM
ajst-31195	34	26	)	)	PUNCT
ajst-31195	34	27	and	and	CCONJ
ajst-31195	34	28	imagenet	imagenet	NOUN
ajst-31195	34	29	dataset	dataset	NOUN
ajst-31195	34	30	(	(	PUNCT
ajst-31195	34	31	image	image	NOUN
ajst-31195	34	32	resolution	resolution	NOUN
ajst-31195	34	33	of	of	ADP
ajst-31195	34	34	224	224	NUM
ajst-31195	34	35	)	)	PUNCT
ajst-31195	34	36	to	to	PART
ajst-31195	34	37	accurately	accurately	ADV
ajst-31195	34	38	recognize	recognize	VERB
ajst-31195	34	39	apples	apple	NOUN
ajst-31195	34	40	,	,	PUNCT
ajst-31195	34	41	thus	thus	ADV
ajst-31195	34	42	obtaining	obtain	VERB
ajst-31195	34	43	information	information	NOUN
ajst-31195	34	44	on	on	ADP
ajst-31195	34	45	the	the	DET
ajst-31195	34	46	number	number	NOUN
ajst-31195	34	47	of	of	ADP
ajst-31195	34	48	apples	apple	NOUN
ajst-31195	34	49	and	and	CCONJ
ajst-31195	34	50	drawing	draw	VERB
ajst-31195	34	51	a	a	DET
ajst-31195	34	52	histogram	histogram	NOUN
ajst-31195	34	53	of	of	ADP
ajst-31195	34	54	apple	apple	NOUN
ajst-31195	34	55	distribution	distribution	NOUN
ajst-31195	34	56	.	.	PUNCT
ajst-31195	35	1	2.2	2.2	NUM
ajst-31195	35	2	.	.	PUNCT
ajst-31195	36	1	data	datum	NOUN
ajst-31195	36	2	preprocessing	preprocesse	VERB
ajst-31195	36	3	images	image	NOUN
ajst-31195	36	4	captured	capture	VERB
ajst-31195	36	5	in	in	ADP
ajst-31195	36	6	natural	natural	ADJ
ajst-31195	36	7	environments	environment	NOUN
ajst-31195	36	8	often	often	ADV
ajst-31195	36	9	contain	contain	VERB
ajst-31195	36	10	significant	significant	ADJ
ajst-31195	36	11	noise	noise	NOUN
ajst-31195	36	12	,	,	PUNCT
ajst-31195	36	13	which	which	PRON
ajst-31195	36	14	can	can	AUX
ajst-31195	36	15	obscure	obscure	VERB
ajst-31195	36	16	partial	partial	ADJ
ajst-31195	36	17	feature	feature	NOUN
ajst-31195	36	18	information	information	NOUN
ajst-31195	36	19	and	and	CCONJ
ajst-31195	36	20	reduce	reduce	VERB
ajst-31195	36	21	the	the	DET
ajst-31195	36	22	contrast	contrast	NOUN
ajst-31195	36	23	between	between	ADP
ajst-31195	36	24	apples	apple	NOUN
ajst-31195	36	25	and	and	CCONJ
ajst-31195	36	26	the	the	DET
ajst-31195	36	27	background	background	NOUN
ajst-31195	36	28	.	.	PUNCT
ajst-31195	37	1	this	this	PRON
ajst-31195	37	2	makes	make	VERB
ajst-31195	37	3	it	it	PRON
ajst-31195	37	4	challenging	challenge	VERB
ajst-31195	37	5	for	for	ADP
ajst-31195	37	6	deep	deep	ADJ
ajst-31195	37	7	learning	learning	NOUN
ajst-31195	37	8	models	model	NOUN
ajst-31195	37	9	to	to	PART
ajst-31195	37	10	extract	extract	VERB
ajst-31195	37	11	meaningful	meaningful	ADJ
ajst-31195	37	12	features	feature	NOUN
ajst-31195	37	13	.	.	PUNCT
ajst-31195	38	1	therefore	therefore	ADV
ajst-31195	38	2	,	,	PUNCT
ajst-31195	38	3	prior	prior	ADV
ajst-31195	38	4	to	to	ADP
ajst-31195	38	5	formal	formal	ADJ
ajst-31195	38	6	image	image	NOUN
ajst-31195	38	7	training	training	NOUN
ajst-31195	38	8	,	,	PUNCT
ajst-31195	38	9	preprocessing	preprocessing	NOUN
ajst-31195	38	10	is	be	AUX
ajst-31195	38	11	essential	essential	ADJ
ajst-31195	38	12	to	to	PART
ajst-31195	38	13	mitigate	mitigate	VERB
ajst-31195	38	14	the	the	DET
ajst-31195	38	15	impact	impact	NOUN
ajst-31195	38	16	of	of	ADP
ajst-31195	38	17	noise	noise	NOUN
ajst-31195	38	18	on	on	ADP
ajst-31195	38	19	the	the	DET
ajst-31195	38	20	model	model	NOUN
ajst-31195	38	21	,	,	PUNCT
ajst-31195	38	22	accelerate	accelerate	VERB
ajst-31195	38	23	its	its	PRON
ajst-31195	38	24	convergence	convergence	NOUN
ajst-31195	38	25	speed	speed	NOUN
ajst-31195	38	26	,	,	PUNCT
ajst-31195	38	27	and	and	CCONJ
ajst-31195	38	28	enhance	enhance	VERB
ajst-31195	38	29	overall	overall	ADJ
ajst-31195	38	30	performance	performance	NOUN
ajst-31195	38	31	.	.	PUNCT
ajst-31195	39	1	as	as	SCONJ
ajst-31195	39	2	shown	show	VERB
ajst-31195	39	3	in	in	ADP
ajst-31195	39	4	figure	figure	NOUN
ajst-31195	39	5	1	1	NUM
ajst-31195	39	6	,	,	PUNCT
ajst-31195	39	7	in	in	ADP
ajst-31195	39	8	this	this	DET
ajst-31195	39	9	study	study	NOUN
ajst-31195	39	10	,	,	PUNCT
ajst-31195	39	11	image	image	NOUN
ajst-31195	39	12	sharpening	sharpening	NOUN
ajst-31195	39	13	followed	follow	VERB
ajst-31195	39	14	by	by	ADP
ajst-31195	39	15	median	median	ADJ
ajst-31195	39	16	filtering	filtering	NOUN
ajst-31195	39	17	is	be	AUX
ajst-31195	39	18	employed	employ	VERB
ajst-31195	39	19	as	as	ADP
ajst-31195	39	20	the	the	DET
ajst-31195	39	21	preliminary	preliminary	ADJ
ajst-31195	39	22	preprocessing	preprocessing	NOUN
ajst-31195	39	23	step	step	NOUN
ajst-31195	39	24	to	to	PART
ajst-31195	39	25	refine	refine	VERB
ajst-31195	39	26	the	the	DET
ajst-31195	39	27	image	image	NOUN
ajst-31195	39	28	quality[4	quality[4	NOUN
ajst-31195	39	29	]	]	PUNCT
ajst-31195	39	30	.	.	PUNCT
ajst-31195	40	1	original	original	ADJ
ajst-31195	40	2	sharpen	sharpen	ADJ
ajst-31195	40	3	median	median	NOUN
ajst-31195	40	4	filter	filter	NOUN
ajst-31195	40	5	figure	figure	NOUN
ajst-31195	40	6	1	1	NUM
ajst-31195	40	7	.	.	PUNCT
ajst-31195	40	8	image	image	NOUN
ajst-31195	40	9	pre	pre	ADJ
ajst-31195	40	10	-	-	ADJ
ajst-31195	40	11	processing	processing	ADJ
ajst-31195	40	12	1	1	NUM
ajst-31195	40	13	)	)	PUNCT
ajst-31195	40	14	image	image	NOUN
ajst-31195	40	15	sharpening	sharpen	VERB
ajst-31195	40	16	in	in	ADP
ajst-31195	40	17	this	this	DET
ajst-31195	40	18	paper	paper	NOUN
ajst-31195	40	19	,	,	PUNCT
ajst-31195	40	20	in	in	ADP
ajst-31195	40	21	order	order	NOUN
ajst-31195	40	22	to	to	PART
ajst-31195	40	23	enhance	enhance	VERB
ajst-31195	40	24	the	the	DET
ajst-31195	40	25	edges	edge	NOUN
ajst-31195	40	26	and	and	CCONJ
ajst-31195	40	27	contours	contours	NOUN
ajst-31195	40	28	of	of	ADP
ajst-31195	40	29	the	the	DET
ajst-31195	40	30	image	image	NOUN
ajst-31195	40	31	to	to	PART
ajst-31195	40	32	be	be	AUX
ajst-31195	40	33	able	able	ADJ
ajst-31195	40	34	to	to	PART
ajst-31195	40	35	identify	identify	VERB
ajst-31195	40	36	the	the	DET
ajst-31195	40	37	apples	apple	NOUN
ajst-31195	40	38	better	well	ADV
ajst-31195	40	39	,	,	PUNCT
ajst-31195	40	40	image	image	NOUN
ajst-31195	40	41	sharpening	sharpening	NOUN
ajst-31195	40	42	is	be	AUX
ajst-31195	40	43	achieved	achieve	VERB
ajst-31195	40	44	by	by	ADP
ajst-31195	40	45	increasing	increase	VERB
ajst-31195	40	46	the	the	DET
ajst-31195	40	47	difference	difference	NOUN
ajst-31195	40	48	of	of	ADP
ajst-31195	40	49	pixels	pixel	NOUN
ajst-31195	40	50	between	between	ADP
ajst-31195	40	51	neighbors	neighbor	NOUN
ajst-31195	40	52	.	.	PUNCT
ajst-31195	41	1	the	the	DET
ajst-31195	41	2	essence	essence	NOUN
ajst-31195	41	3	of	of	ADP
ajst-31195	41	4	image	image	NOUN
ajst-31195	41	5	sharpening	sharpening	NOUN
ajst-31195	41	6	is	be	AUX
ajst-31195	41	7	high	high	ADJ
ajst-31195	41	8	pass	pass	NOUN
ajst-31195	41	9	filtering	filtering	NOUN
ajst-31195	41	10	,	,	PUNCT
ajst-31195	41	11	which	which	PRON
ajst-31195	41	12	is	be	AUX
ajst-31195	41	13	opposite	opposite	ADJ
ajst-31195	41	14	to	to	PART
ajst-31195	41	15	low	low	ADJ
ajst-31195	41	16	pass	pass	NOUN
ajst-31195	41	17	filtering	filtering	NOUN
ajst-31195	41	18	,	,	PUNCT
ajst-31195	41	19	which	which	PRON
ajst-31195	41	20	blurs	blur	VERB
ajst-31195	41	21	the	the	DET
ajst-31195	41	22	image	image	NOUN
ajst-31195	41	23	,	,	PUNCT
ajst-31195	41	24	whereas	whereas	SCONJ
ajst-31195	41	25	sharpening	sharpen	VERB
ajst-31195	41	26	filter	filter	NOUN
ajst-31195	41	27	increases	increase	VERB
ajst-31195	41	28	the	the	DET
ajst-31195	41	29	difference	difference	NOUN
ajst-31195	41	30	of	of	ADP
ajst-31195	41	31	pixels	pixel	NOUN
ajst-31195	41	32	between	between	ADP
ajst-31195	41	33	neighbors	neighbor	NOUN
ajst-31195	41	34	by	by	ADP
ajst-31195	41	35	using	use	VERB
ajst-31195	41	36	the	the	DET
ajst-31195	41	37	differentiation	differentiation	NOUN
ajst-31195	41	38	of	of	ADP
ajst-31195	41	39	neighbors	neighbor	NOUN
ajst-31195	41	40	as	as	ADP
ajst-31195	41	41	an	an	DET
ajst-31195	41	42	operator	operator	NOUN
ajst-31195	41	43	to	to	PART
ajst-31195	41	44	make	make	VERB
ajst-31195	41	45	the	the	DET
ajst-31195	41	46	mutated	mutate	VERB
ajst-31195	41	47	parts	part	NOUN
ajst-31195	41	48	of	of	ADP
ajst-31195	41	49	the	the	DET
ajst-31195	41	50	image	image	NOUN
ajst-31195	41	51	more	more	ADV
ajst-31195	41	52	visible	visible	ADJ
ajst-31195	41	53	,	,	PUNCT
ajst-31195	41	54	thus	thus	ADV
ajst-31195	41	55	improving	improve	VERB
ajst-31195	41	56	the	the	DET
ajst-31195	41	57	recognition	recognition	NOUN
ajst-31195	41	58	rate	rate	NOUN
ajst-31195	41	59	of	of	ADP
ajst-31195	41	60	effective	effective	ADJ
ajst-31195	41	61	features	feature	NOUN
ajst-31195	41	62	of	of	ADP
ajst-31195	41	63	the	the	DET
ajst-31195	41	64	image	image	NOUN
ajst-31195	41	65	.	.	PUNCT
ajst-31195	42	1	2	2	X
ajst-31195	42	2	)	)	PUNCT
ajst-31195	42	3	median	median	ADJ
ajst-31195	42	4	value	value	NOUN
ajst-31195	42	5	filtering	filter	VERB
ajst-31195	42	6	in	in	ADP
ajst-31195	42	7	this	this	DET
ajst-31195	42	8	paper	paper	NOUN
ajst-31195	42	9	,	,	PUNCT
ajst-31195	42	10	median	median	ADJ
ajst-31195	42	11	filtering	filtering	NOUN
ajst-31195	42	12	is	be	AUX
ajst-31195	42	13	chosen	choose	VERB
ajst-31195	42	14	to	to	PART
ajst-31195	42	15	suppress	suppress	VERB
ajst-31195	42	16	the	the	DET
ajst-31195	42	17	noise	noise	NOUN
ajst-31195	42	18	in	in	ADP
ajst-31195	42	19	the	the	DET
ajst-31195	42	20	image	image	NOUN
ajst-31195	42	21	and	and	CCONJ
ajst-31195	42	22	reduce	reduce	VERB
ajst-31195	42	23	the	the	DET
ajst-31195	42	24	effect	effect	NOUN
ajst-31195	42	25	of	of	ADP
ajst-31195	42	26	background	background	NOUN
ajst-31195	42	27	on	on	ADP
ajst-31195	42	28	the	the	DET
ajst-31195	42	29	model	model	NOUN
ajst-31195	42	30	training	training	NOUN
ajst-31195	42	31	.	.	PUNCT
ajst-31195	43	1	in	in	ADP
ajst-31195	43	2	this	this	DET
ajst-31195	43	3	study	study	NOUN
ajst-31195	43	4	,	,	PUNCT
ajst-31195	43	5	the	the	DET
ajst-31195	43	6	primary	primary	ADJ
ajst-31195	43	7	objective	objective	NOUN
ajst-31195	43	8	is	be	AUX
ajst-31195	43	9	to	to	PART
ajst-31195	43	10	accurately	accurately	ADV
ajst-31195	43	11	recognize	recognize	VERB
ajst-31195	43	12	apple	apple	NOUN
ajst-31195	43	13	images	image	NOUN
ajst-31195	43	14	while	while	SCONJ
ajst-31195	43	15	preserving	preserve	VERB
ajst-31195	43	16	essential	essential	ADJ
ajst-31195	43	17	image	image	NOUN
ajst-31195	43	18	details	detail	NOUN
ajst-31195	43	19	and	and	CCONJ
ajst-31195	43	20	effectively	effectively	ADV
ajst-31195	43	21	eliminating	eliminate	VERB
ajst-31195	43	22	noise	noise	NOUN
ajst-31195	43	23	.	.	PUNCT
ajst-31195	44	1	the	the	DET
ajst-31195	44	2	median	median	ADJ
ajst-31195	44	3	filter	filter	NOUN
ajst-31195	44	4	removes	remove	VERB
ajst-31195	44	5	the	the	DET
ajst-31195	44	6	noise	noise	NOUN
ajst-31195	44	7	and	and	CCONJ
ajst-31195	44	8	avoids	avoid	VERB
ajst-31195	44	9	destroying	destroy	VERB
ajst-31195	44	10	the	the	DET
ajst-31195	44	11	details	detail	NOUN
ajst-31195	44	12	of	of	ADP
ajst-31195	44	13	the	the	DET
ajst-31195	44	14	image	image	NOUN
ajst-31195	44	15	.	.	PUNCT
ajst-31195	45	1	compared	compare	VERB
ajst-31195	45	2	with	with	ADP
ajst-31195	45	3	the	the	DET
ajst-31195	45	4	median	median	ADJ
ajst-31195	45	5	filter	filter	NOUN
ajst-31195	45	6	,	,	PUNCT
ajst-31195	45	7	the	the	DET
ajst-31195	45	8	mean	mean	ADJ
ajst-31195	45	9	filter	filter	NOUN
ajst-31195	45	10	,	,	PUNCT
ajst-31195	45	11	although	although	SCONJ
ajst-31195	45	12	simple	simple	ADJ
ajst-31195	45	13	and	and	CCONJ
ajst-31195	45	14	easy	easy	ADJ
ajst-31195	45	15	to	to	PART
ajst-31195	45	16	smooth	smooth	VERB
ajst-31195	45	17	the	the	DET
ajst-31195	45	18	noise	noise	NOUN
ajst-31195	45	19	of	of	ADP
ajst-31195	45	20	the	the	DET
ajst-31195	45	21	image	image	NOUN
ajst-31195	45	22	,	,	PUNCT
ajst-31195	45	23	may	may	AUX
ajst-31195	45	24	make	make	VERB
ajst-31195	45	25	the	the	DET
ajst-31195	45	26	image	image	NOUN
ajst-31195	45	27	blurred	blur	VERB
ajst-31195	45	28	and	and	CCONJ
ajst-31195	45	29	can	can	AUX
ajst-31195	45	30	not	not	PART
ajst-31195	45	31	remove	remove	VERB
ajst-31195	45	32	the	the	DET
ajst-31195	45	33	noise	noise	NOUN
ajst-31195	45	34	points	point	NOUN
ajst-31195	45	35	well	well	ADV
ajst-31195	45	36	.	.	PUNCT
ajst-31195	46	1	gaussian	gaussian	ADJ
ajst-31195	46	2	filter	filter	NOUN
ajst-31195	46	3	can	can	AUX
ajst-31195	46	4	be	be	AUX
ajst-31195	46	5	adjusted	adjust	VERB
ajst-31195	46	6	by	by	ADP
ajst-31195	46	7	the	the	DET
ajst-31195	46	8	size	size	NOUN
ajst-31195	46	9	of	of	ADP
ajst-31195	46	10	the	the	DET
ajst-31195	46	11	standard	standard	ADJ
ajst-31195	46	12	deviation	deviation	NOUN
ajst-31195	46	13	,	,	PUNCT
ajst-31195	46	14	can	can	AUX
ajst-31195	46	15	achieve	achieve	VERB
ajst-31195	46	16	different	different	ADJ
ajst-31195	46	17	degrees	degree	NOUN
ajst-31195	46	18	of	of	ADP
ajst-31195	46	19	smoothing	smooth	VERB
ajst-31195	46	20	and	and	CCONJ
ajst-31195	46	21	denoising	denoising	NOUN
ajst-31195	46	22	effect	effect	NOUN
ajst-31195	46	23	but	but	CCONJ
ajst-31195	46	24	in	in	ADP
ajst-31195	46	25	the	the	DET
ajst-31195	46	26	processing	processing	NOUN
ajst-31195	46	27	of	of	ADP
ajst-31195	46	28	"	"	PUNCT
ajst-31195	46	29	details	detail	NOUN
ajst-31195	46	30	"	"	PUNCT
ajst-31195	46	31	or	or	CCONJ
ajst-31195	46	32	"	"	PUNCT
ajst-31195	46	33	edges	edge	NOUN
ajst-31195	46	34	"	"	PUNCT
ajst-31195	46	35	of	of	ADP
ajst-31195	46	36	the	the	DET
ajst-31195	46	37	image	image	NOUN
ajst-31195	46	38	,	,	PUNCT
ajst-31195	46	39	will	will	AUX
ajst-31195	46	40	blur	blur	VERB
ajst-31195	46	41	the	the	DET
ajst-31195	46	42	edges	edge	NOUN
ajst-31195	46	43	.	.	PUNCT
ajst-31195	47	1	therefore	therefore	ADV
ajst-31195	47	2	,	,	PUNCT
ajst-31195	47	3	compared	compare	VERB
ajst-31195	47	4	with	with	ADP
ajst-31195	47	5	the	the	DET
ajst-31195	47	6	mean	mean	ADJ
ajst-31195	47	7	filter	filter	NOUN
ajst-31195	47	8	and	and	CCONJ
ajst-31195	47	9	gaussian	gaussian	ADJ
ajst-31195	47	10	filter	filter	NOUN
ajst-31195	47	11	,	,	PUNCT
ajst-31195	47	12	the	the	DET
ajst-31195	47	13	median	median	ADJ
ajst-31195	47	14	filter	filter	NOUN
ajst-31195	47	15	may	may	AUX
ajst-31195	47	16	be	be	AUX
ajst-31195	47	17	more	more	ADV
ajst-31195	47	18	suitable	suitable	ADJ
ajst-31195	47	19	for	for	ADP
ajst-31195	47	20	the	the	DET
ajst-31195	47	21	apple	apple	NOUN
ajst-31195	47	22	image	image	NOUN
ajst-31195	47	23	recognition	recognition	NOUN
ajst-31195	47	24	in	in	ADP
ajst-31195	47	25	this	this	DET
ajst-31195	47	26	paper	paper	NOUN
ajst-31195	47	27	.	.	PUNCT
ajst-31195	48	1	2.3	2.3	NUM
ajst-31195	48	2	.	.	PUNCT
ajst-31195	48	3	yolov8x	yolov8x	NOUN
ajst-31195	48	4	-	-	PUNCT
ajst-31195	48	5	seg	seg	PROPN
ajst-31195	48	6	training	training	NOUN
ajst-31195	48	7	model	model	NOUN
ajst-31195	48	8	the	the	DET
ajst-31195	48	9	training	training	NOUN
ajst-31195	48	10	process	process	NOUN
ajst-31195	48	11	of	of	ADP
ajst-31195	48	12	the	the	DET
ajst-31195	48	13	yolov8x	yolov8x	PROPN
ajst-31195	48	14	-	-	PUNCT
ajst-31195	48	15	seg	seg	PROPN
ajst-31195	48	16	model	model	NOUN
ajst-31195	48	17	consists	consist	VERB
ajst-31195	48	18	of	of	ADP
ajst-31195	48	19	two	two	NUM
ajst-31195	48	20	phases	phase	NOUN
ajst-31195	48	21	:	:	PUNCT
ajst-31195	48	22	pre	pre	ADJ
ajst-31195	48	23	-	-	ADJ
ajst-31195	48	24	training	training	ADJ
ajst-31195	48	25	and	and	CCONJ
ajst-31195	48	26	fine	fine	ADV
ajst-31195	48	27	-	-	PUNCT
ajst-31195	48	28	tuning	tuning	NOUN
ajst-31195	48	29	.	.	PUNCT
ajst-31195	49	1	the	the	DET
ajst-31195	49	2	pre	pre	ADJ
ajst-31195	49	3	-	-	ADJ
ajst-31195	49	4	training	training	ADJ
ajst-31195	49	5	phase	phase	NOUN
ajst-31195	49	6	uses	use	VERB
ajst-31195	49	7	a	a	DET
ajst-31195	49	8	large	large	ADJ
ajst-31195	49	9	-	-	PUNCT
ajst-31195	49	10	scale	scale	NOUN
ajst-31195	49	11	image	image	NOUN
ajst-31195	49	12	dataset	dataset	VERB
ajst-31195	49	13	for	for	ADP
ajst-31195	49	14	unsupervised	unsupervised	ADJ
ajst-31195	49	15	training	training	NOUN
ajst-31195	49	16	to	to	PART
ajst-31195	49	17	learn	learn	VERB
ajst-31195	49	18	a	a	DET
ajst-31195	49	19	generic	generic	ADJ
ajst-31195	49	20	feature	feature	NOUN
ajst-31195	49	21	representation	representation	NOUN
ajst-31195	49	22	.	.	PUNCT
ajst-31195	50	1	the	the	DET
ajst-31195	50	2	model	model	NOUN
ajst-31195	50	3	is	be	AUX
ajst-31195	50	4	bundled	bundle	VERB
ajst-31195	50	5	with	with	ADP
ajst-31195	50	6	the	the	DET
ajst-31195	50	7	following	follow	VERB
ajst-31195	50	8	pre	pre	ADJ
ajst-31195	50	9	-	-	ADJ
ajst-31195	50	10	trained	train	VERB
ajst-31195	50	11	models	model	NOUN
ajst-31195	50	12	:	:	PUNCT
ajst-31195	50	13	an	an	DET
ajst-31195	50	14	object	object	NOUN
ajst-31195	50	15	detection	detection	NOUN
ajst-31195	50	16	checkpoint	checkpoint	NOUN
ajst-31195	50	17	trained	train	VERB
ajst-31195	50	18	on	on	ADP
ajst-31195	50	19	the	the	DET
ajst-31195	50	20	coco	coco	PROPN
ajst-31195	50	21	detection	detection	NOUN
ajst-31195	50	22	dataset	dataset	VERB
ajst-31195	50	23	with	with	ADP
ajst-31195	50	24	an	an	DET
ajst-31195	50	25	image	image	NOUN
ajst-31195	50	26	resolution	resolution	NOUN
ajst-31195	50	27	of	of	ADP
ajst-31195	50	28	640	640	NUM
ajst-31195	50	29	,	,	PUNCT
ajst-31195	50	30	an	an	DET
ajst-31195	50	31	instance	instance	NOUN
ajst-31195	50	32	segmentation	segmentation	NOUN
ajst-31195	50	33	checkpoint	checkpoint	NOUN
ajst-31195	50	34	trained	train	VERB
ajst-31195	50	35	on	on	ADP
ajst-31195	50	36	the	the	DET
ajst-31195	50	37	coco	coco	PROPN
ajst-31195	50	38	segmentation	segmentation	NOUN
ajst-31195	50	39	dataset	dataset	VERB
ajst-31195	50	40	with	with	ADP
ajst-31195	50	41	an	an	DET
ajst-31195	50	42	image	image	NOUN
ajst-31195	50	43	resolution	resolution	NOUN
ajst-31195	50	44	of	of	ADP
ajst-31195	50	45	640	640	NUM
ajst-31195	50	46	,	,	PUNCT
ajst-31195	50	47	and	and	CCONJ
ajst-31195	50	48	an	an	DET
ajst-31195	50	49	image	image	NOUN
ajst-31195	50	50	classification	classification	NOUN
ajst-31195	50	51	model	model	NOUN
ajst-31195	50	52	pre	pre	VERB
ajst-31195	50	53	-	-	VERB
ajst-31195	50	54	trained	train	VERB
ajst-31195	50	55	on	on	ADP
ajst-31195	50	56	the	the	DET
ajst-31195	50	57	imagenet	imagenet	NOUN
ajst-31195	50	58	dataset	dataset	VERB
ajst-31195	50	59	with	with	ADP
ajst-31195	50	60	an	an	DET
ajst-31195	50	61	image	image	NOUN
ajst-31195	50	62	resolution	resolution	NOUN
ajst-31195	50	63	of	of	ADP
ajst-31195	50	64	224	224	NUM
ajst-31195	50	65	.	.	PUNCT
ajst-31195	51	1	specifically	specifically	ADV
ajst-31195	51	2	,	,	PUNCT
ajst-31195	51	3	the	the	DET
ajst-31195	51	4	yolov8x	yolov8x	PROPN
ajst-31195	51	5	-	-	PUNCT
ajst-31195	51	6	seg	seg	PROPN
ajst-31195	51	7	model	model	NOUN
ajst-31195	51	8	has	have	VERB
ajst-31195	51	9	five	five	NUM
ajst-31195	51	10	pretrained	pretraine	VERB
ajst-31195	51	11	models	model	NOUN
ajst-31195	51	12	for	for	ADP
ajst-31195	51	13	detection	detection	NOUN
ajst-31195	51	14	,	,	PUNCT
ajst-31195	51	15	segmentation	segmentation	NOUN
ajst-31195	51	16	,	,	PUNCT
ajst-31195	51	17	and	and	CCONJ
ajst-31195	51	18	classification	classification	NOUN
ajst-31195	51	19	tasks	task	NOUN
ajst-31195	51	20	.	.	PUNCT
ajst-31195	52	1	among	among	ADP
ajst-31195	52	2	them	they	PRON
ajst-31195	52	3	,	,	PUNCT
ajst-31195	52	4	yolov8	yolov8	PROPN
ajst-31195	52	5	nano	nano	PROPN
ajst-31195	52	6	is	be	AUX
ajst-31195	52	7	the	the	DET
ajst-31195	52	8	fastest	fast	ADJ
ajst-31195	52	9	and	and	CCONJ
ajst-31195	52	10	smallest	smallest	ADJ
ajst-31195	52	11	model	model	NOUN
ajst-31195	52	12	,	,	PUNCT
ajst-31195	52	13	while	while	SCONJ
ajst-31195	52	14	yolov8	yolov8	NOUN
ajst-31195	52	15	extra	extra	VERB
ajst-31195	52	16	large	large	ADJ
ajst-31195	52	17	(	(	PUNCT
ajst-31195	52	18	yolov8x	yolov8x	NOUN
ajst-31195	52	19	-	-	PUNCT
ajst-31195	52	20	seg	seg	PROPN
ajst-31195	52	21	)	)	PUNCT
ajst-31195	52	22	is	be	AUX
ajst-31195	52	23	the	the	DET
ajst-31195	52	24	most	most	ADV
ajst-31195	52	25	accurate	accurate	ADJ
ajst-31195	52	26	but	but	CCONJ
ajst-31195	52	27	slowest	slow	ADJ
ajst-31195	52	28	model	model	NOUN
ajst-31195	52	29	.	.	PUNCT
ajst-31195	53	1	these	these	DET
ajst-31195	53	2	models	model	NOUN
ajst-31195	53	3	can	can	AUX
ajst-31195	53	4	be	be	AUX
ajst-31195	53	5	selected	select	VERB
ajst-31195	53	6	for	for	ADP
ajst-31195	53	7	different	different	ADJ
ajst-31195	53	8	application	application	NOUN
ajst-31195	53	9	scenarios	scenario	NOUN
ajst-31195	53	10	.	.	PUNCT
ajst-31195	54	1	the	the	DET
ajst-31195	54	2	fine	fine	ADV
ajst-31195	54	3	-	-	PUNCT
ajst-31195	54	4	tuning	tune	VERB
ajst-31195	54	5	phase	phase	NOUN
ajst-31195	54	6	then	then	ADV
ajst-31195	54	7	uses	use	VERB
ajst-31195	54	8	labeled	label	VERB
ajst-31195	54	9	target	target	NOUN
ajst-31195	54	10	detection	detection	NOUN
ajst-31195	54	11	datasets	dataset	NOUN
ajst-31195	54	12	to	to	PART
ajst-31195	54	13	supervise	supervise	VERB
ajst-31195	54	14	the	the	DET
ajst-31195	54	15	training	training	NOUN
ajst-31195	54	16	of	of	ADP
ajst-31195	54	17	the	the	DET
ajst-31195	54	18	models	model	NOUN
ajst-31195	54	19	so	so	SCONJ
ajst-31195	54	20	that	that	SCONJ
ajst-31195	54	21	they	they	PRON
ajst-31195	54	22	can	can	AUX
ajst-31195	54	23	be	be	AUX
ajst-31195	54	24	better	well	ADV
ajst-31195	54	25	adapted	adapt	VERB
ajst-31195	54	26	to	to	ADP
ajst-31195	54	27	the	the	DET
ajst-31195	54	28	eye	eye	NOUN
ajst-31195	54	29	detection	detection	NOUN
ajst-31195	54	30	task	task	NOUN
ajst-31195	54	31	.	.	PUNCT
ajst-31195	55	1	the	the	DET
ajst-31195	55	2	yolov8x	yolov8x	PROPN
ajst-31195	55	3	-	-	PUNCT
ajst-31195	55	4	seg	seg	PROPN
ajst-31195	55	5	model	model	NOUN
ajst-31195	55	6	was	be	AUX
ajst-31195	55	7	selected	select	VERB
ajst-31195	55	8	for	for	ADP
ajst-31195	55	9	this	this	DET
ajst-31195	55	10	study	study	NOUN
ajst-31195	55	11	due	due	ADP
ajst-31195	55	12	to	to	ADP
ajst-31195	55	13	its	its	PRON
ajst-31195	55	14	exceptional	exceptional	ADJ
ajst-31195	55	15	capability	capability	NOUN
ajst-31195	55	16	in	in	ADP
ajst-31195	55	17	image	image	NOUN
ajst-31195	55	18	processing	processing	NOUN
ajst-31195	55	19	,	,	PUNCT
ajst-31195	55	20	enabling	enable	VERB
ajst-31195	55	21	rapid	rapid	ADJ
ajst-31195	55	22	and	and	CCONJ
ajst-31195	55	23	accurate	accurate	ADJ
ajst-31195	55	24	extraction	extraction	NOUN
ajst-31195	55	25	of	of	ADP
ajst-31195	55	26	apple	apple	NOUN
ajst-31195	55	27	features	feature	NOUN
ajst-31195	55	28	.	.	PUNCT
ajst-31195	56	1	this	this	DET
ajst-31195	56	2	model	model	NOUN
ajst-31195	56	3	facilitates	facilitate	VERB
ajst-31195	56	4	efficient	efficient	ADJ
ajst-31195	56	5	and	and	CCONJ
ajst-31195	56	6	precise	precise	ADJ
ajst-31195	56	7	statistical	statistical	ADJ
ajst-31195	56	8	analysis	analysis	NOUN
ajst-31195	56	9	of	of	ADP
ajst-31195	56	10	apple	apple	NOUN
ajst-31195	56	11	quantities	quantity	NOUN
ajst-31195	56	12	,	,	PUNCT
ajst-31195	56	13	making	make	VERB
ajst-31195	56	14	it	it	PRON
ajst-31195	56	15	an	an	DET
ajst-31195	56	16	ideal	ideal	ADJ
ajst-31195	56	17	choice	choice	NOUN
ajst-31195	56	18	for	for	ADP
ajst-31195	56	19	achieving	achieve	VERB
ajst-31195	56	20	robust	robust	ADJ
ajst-31195	56	21	and	and	CCONJ
ajst-31195	56	22	reliable	reliable	ADJ
ajst-31195	56	23	results	result	NOUN
ajst-31195	56	24	in	in	ADP
ajst-31195	56	25	this	this	DET
ajst-31195	56	26	context	context	NOUN
ajst-31195	56	27	.	.	PUNCT
ajst-31195	57	1	its	its	PRON
ajst-31195	57	2	basic	basic	ADJ
ajst-31195	57	3	idea	idea	NOUN
ajst-31195	57	4	is	be	AUX
ajst-31195	57	5	to	to	PART
ajst-31195	57	6	process	process	VERB
ajst-31195	57	7	and	and	CCONJ
ajst-31195	57	8	feature	feature	NOUN
ajst-31195	57	9	extract	extract	VERB
ajst-31195	57	10	the	the	DET
ajst-31195	57	11	input	input	NOUN
ajst-31195	57	12	data	datum	NOUN
ajst-31195	57	13	through	through	ADP
ajst-31195	57	14	components	component	NOUN
ajst-31195	57	15	such	such	ADJ
ajst-31195	57	16	as	as	ADP
ajst-31195	57	17	multi	multi	ADJ
ajst-31195	57	18	-	-	ADJ
ajst-31195	57	19	layer	layer	ADJ
ajst-31195	57	20	convolutional	convolutional	ADJ
ajst-31195	57	21	layers	layer	NOUN
ajst-31195	57	22	,	,	PUNCT
ajst-31195	57	23	pooling	pool	VERB
ajst-31195	57	24	layers	layer	NOUN
ajst-31195	57	25	and	and	CCONJ
ajst-31195	57	26	fully	fully	ADV
ajst-31195	57	27	connected	connected	ADJ
ajst-31195	57	28	layers	layer	NOUN
ajst-31195	57	29	,	,	PUNCT
ajst-31195	57	30	and	and	CCONJ
ajst-31195	57	31	then	then	ADV
ajst-31195	57	32	count	count	VERB
ajst-31195	57	33	the	the	DET
ajst-31195	57	34	number	number	NOUN
ajst-31195	57	35	of	of	ADP
ajst-31195	57	36	apples	apple	NOUN
ajst-31195	57	37	in	in	ADP
ajst-31195	57	38	each	each	DET
ajst-31195	57	39	image	image	NOUN
ajst-31195	57	40	.	.	PUNCT
ajst-31195	58	1	the	the	DET
ajst-31195	58	2	yolov8x	yolov8x	PROPN
ajst-31195	58	3	-	-	PUNCT
ajst-31195	58	4	seg	seg	PROPN
ajst-31195	58	5	model	model	PROPN
ajst-31195	58	6	flowchart	flowchart	PROPN
ajst-31195	58	7	is	be	AUX
ajst-31195	58	8	shown	show	VERB
ajst-31195	58	9	in	in	ADP
ajst-31195	58	10	figure	figure	NOUN
ajst-31195	58	11	2	2	NUM
ajst-31195	58	12	.	.	SYM
ajst-31195	58	13	19	19	NUM
ajst-31195	58	14	figure	figure	NOUN
ajst-31195	58	15	2	2	NUM
ajst-31195	58	16	.	.	PUNCT
ajst-31195	59	1	the	the	DET
ajst-31195	59	2	flow	flow	NOUN
ajst-31195	59	3	chart	chart	NOUN
ajst-31195	59	4	of	of	ADP
ajst-31195	59	5	yolov8x	yolov8x	PROPN
ajst-31195	59	6	-	-	PUNCT
ajst-31195	59	7	seg	seg	PROPN
ajst-31195	59	8	1	1	NUM
ajst-31195	59	9	)	)	PUNCT
ajst-31195	59	10	convolutional	convolutional	ADJ
ajst-31195	59	11	layer	layer	NOUN
ajst-31195	59	12	specifically	specifically	ADV
ajst-31195	59	13	,	,	PUNCT
ajst-31195	59	14	the	the	DET
ajst-31195	59	15	convolutional	convolutional	ADJ
ajst-31195	59	16	layer	layer	NOUN
ajst-31195	59	17	detects	detect	NOUN
ajst-31195	59	18	local	local	ADJ
ajst-31195	59	19	features	feature	NOUN
ajst-31195	59	20	in	in	ADP
ajst-31195	59	21	the	the	DET
ajst-31195	59	22	input	input	NOUN
ajst-31195	59	23	by	by	ADP
ajst-31195	59	24	convolving	convolve	VERB
ajst-31195	59	25	the	the	DET
ajst-31195	59	26	input	input	NOUN
ajst-31195	59	27	with	with	ADP
ajst-31195	59	28	a	a	DET
ajst-31195	59	29	set	set	NOUN
ajst-31195	59	30	of	of	ADP
ajst-31195	59	31	convolutional	convolutional	ADJ
ajst-31195	59	32	kernels	kernel	NOUN
ajst-31195	59	33	(	(	PUNCT
ajst-31195	59	34	or	or	CCONJ
ajst-31195	59	35	filters	filter	NOUN
ajst-31195	59	36	)	)	PUNCT
ajst-31195	59	37	.	.	PUNCT
ajst-31195	60	1	each	each	DET
ajst-31195	60	2	neuron	neuron	NOUN
ajst-31195	60	3	in	in	ADP
ajst-31195	60	4	the	the	DET
ajst-31195	60	5	convolutional	convolutional	ADJ
ajst-31195	60	6	layer	layer	NOUN
ajst-31195	60	7	is	be	AUX
ajst-31195	60	8	connected	connect	VERB
ajst-31195	60	9	to	to	ADP
ajst-31195	60	10	only	only	ADV
ajst-31195	60	11	one	one	NUM
ajst-31195	60	12	local	local	ADJ
ajst-31195	60	13	region	region	NOUN
ajst-31195	60	14	of	of	ADP
ajst-31195	60	15	the	the	DET
ajst-31195	60	16	input	input	NOUN
ajst-31195	60	17	data	datum	NOUN
ajst-31195	60	18	,	,	PUNCT
ajst-31195	60	19	which	which	PRON
ajst-31195	60	20	greatly	greatly	ADV
ajst-31195	60	21	reduces	reduce	VERB
ajst-31195	60	22	the	the	DET
ajst-31195	60	23	number	number	NOUN
ajst-31195	60	24	of	of	ADP
ajst-31195	60	25	parameters	parameter	NOUN
ajst-31195	60	26	in	in	ADP
ajst-31195	60	27	the	the	DET
ajst-31195	60	28	model	model	NOUN
ajst-31195	60	29	.	.	PUNCT
ajst-31195	61	1	this	this	DET
ajst-31195	61	2	paper	paper	NOUN
ajst-31195	61	3	assumes	assume	VERB
ajst-31195	61	4	that	that	SCONJ
ajst-31195	61	5	𝑊	𝑊	PROPN
ajst-31195	61	6	,	,	PUNCT
ajst-31195	61	7	and	and	CCONJ
ajst-31195	61	8	𝑏	𝑏	NOUN
ajst-31195	61	9	,	,	PUNCT
ajst-31195	61	10	denote	denote	VERB
ajst-31195	61	11	the	the	DET
ajst-31195	61	12	weight	weight	NOUN
ajst-31195	61	13	of	of	ADP
ajst-31195	61	14	the	the	DET
ajst-31195	61	15	𝑗	𝑗	INTJ
ajst-31195	61	16	th	th	X
ajst-31195	61	17	convolutional	convolutional	ADJ
ajst-31195	61	18	kernel	kernel	NOUN
ajst-31195	61	19	corresponding	correspond	VERB
ajst-31195	61	20	to	to	ADP
ajst-31195	61	21	the	the	DET
ajst-31195	61	22	𝑖th	𝑖th	NOUN
ajst-31195	61	23	feature	feature	NOUN
ajst-31195	61	24	mapping	mapping	NOUN
ajst-31195	61	25	in	in	ADP
ajst-31195	61	26	layer	layer	NOUN
ajst-31195	61	27	i	i	PRON
ajst-31195	61	28	and	and	CCONJ
ajst-31195	61	29	the	the	DET
ajst-31195	61	30	bias	bias	NOUN
ajst-31195	61	31	of	of	ADP
ajst-31195	61	32	the	the	DET
ajst-31195	61	33	𝑗th	𝑗th	NOUN
ajst-31195	61	34	convolutional	convolutional	ADJ
ajst-31195	61	35	kernel	kernel	NOUN
ajst-31195	61	36	corresponding	correspond	VERB
ajst-31195	61	37	to	to	ADP
ajst-31195	61	38	the	the	DET
ajst-31195	61	39	𝑖th	𝑖th	X
ajst-31195	61	40	convolutional	convolutional	ADJ
ajst-31195	61	41	kernel	kernel	NOUN
ajst-31195	61	42	in	in	ADP
ajst-31195	61	43	layer	layer	NOUN
ajst-31195	61	44	i	i	PRON
ajst-31195	61	45	,	,	PUNCT
ajst-31195	61	46	respectively	respectively	ADV
ajst-31195	61	47	,	,	PUNCT
ajst-31195	61	48	and	and	CCONJ
ajst-31195	61	49	that	that	SCONJ
ajst-31195	61	50	𝑥	𝑥	PROPN
ajst-31195	61	51	is	be	AUX
ajst-31195	61	52	the	the	DET
ajst-31195	61	53	input	input	NOUN
ajst-31195	61	54	of	of	ADP
ajst-31195	61	55	the	the	DET
ajst-31195	61	56	𝑖th	𝑖th	NOUN
ajst-31195	61	57	feature	feature	NOUN
ajst-31195	61	58	mapping	mapping	NOUN
ajst-31195	61	59	in	in	ADP
ajst-31195	61	60	layer	layer	NOUN
ajst-31195	61	61	i	i	PRON
ajst-31195	61	62	1	1	X
ajst-31195	61	63	.	.	PUNCT
ajst-31195	62	1	the	the	DET
ajst-31195	62	2	specific	specific	ADJ
ajst-31195	62	3	formula	formula	NOUN
ajst-31195	62	4	for	for	ADP
ajst-31195	62	5	the	the	DET
ajst-31195	62	6	convolution	convolution	NOUN
ajst-31195	62	7	operation	operation	NOUN
ajst-31195	62	8	is	be	AUX
ajst-31195	62	9	:	:	PUNCT
ajst-31195	62	10	𝑦	𝑦	NUM
ajst-31195	62	11	𝑓	𝑓	PRON
ajst-31195	62	12	∑	∑	PUNCT
ajst-31195	62	13	𝑊	𝑊	PROPN
ajst-31195	62	14	,	,	PUNCT
ajst-31195	62	15	∗	∗	VERB
ajst-31195	62	16	𝑥	𝑥	PRON
ajst-31195	62	17	𝑏	𝑏	PROPN
ajst-31195	62	18	(	(	PUNCT
ajst-31195	62	19	1	1	NUM
ajst-31195	62	20	)	)	PUNCT
ajst-31195	62	21	where	where	SCONJ
ajst-31195	62	22	*	*	PUNCT
ajst-31195	62	23	is	be	AUX
ajst-31195	62	24	the	the	DET
ajst-31195	62	25	local	local	ADJ
ajst-31195	62	26	region	region	NOUN
ajst-31195	62	27	input	input	NOUN
ajst-31195	62	28	for	for	ADP
ajst-31195	62	29	convolution	convolution	NOUN
ajst-31195	62	30	operation	operation	NOUN
ajst-31195	62	31	with	with	ADP
ajst-31195	62	32	convolution	convolution	NOUN
ajst-31195	62	33	kernel	kernel	NOUN
ajst-31195	62	34	;	;	PUNCT
ajst-31195	62	35	𝑦	𝑦	PRON
ajst-31195	62	36	denotes	denote	VERB
ajst-31195	62	37	the	the	DET
ajst-31195	62	38	output	output	NOUN
ajst-31195	62	39	of	of	ADP
ajst-31195	62	40	the	the	DET
ajst-31195	62	41	𝑗	𝑗	INTJ
ajst-31195	62	42	th	th	X
ajst-31195	62	43	convolution	convolution	NOUN
ajst-31195	62	44	kernel	kernel	PROPN
ajst-31195	62	45	generated	generate	VERB
ajst-31195	62	46	in	in	ADP
ajst-31195	62	47	layer	layer	NOUN
ajst-31195	62	48	l	l	NOUN
ajst-31195	62	49	;	;	PUNCT
ajst-31195	62	50	𝑓	𝑓	PRON
ajst-31195	62	51	denotes	denote	VERB
ajst-31195	62	52	an	an	DET
ajst-31195	62	53	activation	activation	NOUN
ajst-31195	62	54	function	function	NOUN
ajst-31195	62	55	.	.	PUNCT
ajst-31195	63	1	2	2	X
ajst-31195	63	2	)	)	PUNCT
ajst-31195	63	3	ponding	ponde	VERB
ajst-31195	63	4	layer	layer	NOUN
ajst-31195	63	5	the	the	DET
ajst-31195	63	6	pooling	pool	VERB
ajst-31195	63	7	layer	layer	NOUN
ajst-31195	63	8	plays	play	VERB
ajst-31195	63	9	the	the	DET
ajst-31195	63	10	functions	function	NOUN
ajst-31195	63	11	of	of	ADP
ajst-31195	63	12	extracting	extract	VERB
ajst-31195	63	13	the	the	DET
ajst-31195	63	14	most	most	ADV
ajst-31195	63	15	significant	significant	ADJ
ajst-31195	63	16	features	feature	NOUN
ajst-31195	63	17	,	,	PUNCT
ajst-31195	63	18	retaining	retain	VERB
ajst-31195	63	19	important	important	ADJ
ajst-31195	63	20	information	information	NOUN
ajst-31195	63	21	and	and	CCONJ
ajst-31195	63	22	improving	improve	VERB
ajst-31195	63	23	computational	computational	ADJ
ajst-31195	63	24	efficiency	efficiency	NOUN
ajst-31195	63	25	in	in	ADP
ajst-31195	63	26	the	the	DET
ajst-31195	63	27	yolov8x	yolov8x	NOUN
ajst-31195	63	28	-	-	PUNCT
ajst-31195	63	29	seg	seg	PROPN
ajst-31195	63	30	model	model	NOUN
ajst-31195	63	31	.	.	PUNCT
ajst-31195	64	1	by	by	ADP
ajst-31195	64	2	appropriately	appropriately	ADV
ajst-31195	64	3	configuring	configure	VERB
ajst-31195	64	4	the	the	DET
ajst-31195	64	5	parameters	parameter	NOUN
ajst-31195	64	6	of	of	ADP
ajst-31195	64	7	the	the	DET
ajst-31195	64	8	pooling	pool	VERB
ajst-31195	64	9	layer	layer	NOUN
ajst-31195	64	10	,	,	PUNCT
ajst-31195	64	11	the	the	DET
ajst-31195	64	12	feature	feature	NOUN
ajst-31195	64	13	data	datum	NOUN
ajst-31195	64	14	can	can	AUX
ajst-31195	64	15	be	be	AUX
ajst-31195	64	16	reasonably	reasonably	ADV
ajst-31195	64	17	compressed	compressed	ADJ
ajst-31195	64	18	and	and	CCONJ
ajst-31195	64	19	transformed	transform	VERB
ajst-31195	64	20	to	to	PART
ajst-31195	64	21	improve	improve	VERB
ajst-31195	64	22	the	the	DET
ajst-31195	64	23	performance	performance	NOUN
ajst-31195	64	24	of	of	ADP
ajst-31195	64	25	the	the	DET
ajst-31195	64	26	model	model	NOUN
ajst-31195	64	27	and	and	CCONJ
ajst-31195	64	28	accelerate	accelerate	VERB
ajst-31195	64	29	the	the	DET
ajst-31195	64	30	training	training	NOUN
ajst-31195	64	31	and	and	CCONJ
ajst-31195	64	32	inference	inference	NOUN
ajst-31195	64	33	speed	speed	NOUN
ajst-31195	64	34	.	.	PUNCT
ajst-31195	65	1	the	the	DET
ajst-31195	65	2	specific	specific	ADJ
ajst-31195	65	3	formula	formula	NOUN
ajst-31195	65	4	is	be	AUX
ajst-31195	65	5	as	as	SCONJ
ajst-31195	65	6	follows	follow	VERB
ajst-31195	65	7	:	:	PUNCT
ajst-31195	65	8	𝑃	𝑃	VERB
ajst-31195	65	9	max	max	PROPN
ajst-31195	65	10	𝑦	𝑦	PROPN
ajst-31195	65	11	(	(	PUNCT
ajst-31195	65	12	2	2	NUM
ajst-31195	65	13	)	)	PUNCT
ajst-31195	65	14	where	where	SCONJ
ajst-31195	65	15	𝑃	𝑃	NOUN
ajst-31195	65	16	denotes	denote	VERB
ajst-31195	65	17	the	the	DET
ajst-31195	65	18	maximum	maximum	ADJ
ajst-31195	65	19	pooled	pooled	ADJ
ajst-31195	65	20	output	output	NOUN
ajst-31195	65	21	of	of	ADP
ajst-31195	65	22	the	the	DET
ajst-31195	65	23	𝑗th	𝑗th	NOUN
ajst-31195	65	24	convolutional	convolutional	ADJ
ajst-31195	65	25	kernel	kernel	NOUN
ajst-31195	65	26	in	in	ADP
ajst-31195	65	27	pooling	pool	VERB
ajst-31195	65	28	layer	layer	NOUN
ajst-31195	65	29	l+1;𝑦	l+1;𝑦	PROPN
ajst-31195	65	30	denotes	denote	VERB
ajst-31195	65	31	the	the	DET
ajst-31195	65	32	output	output	NOUN
ajst-31195	65	33	feature	feature	NOUN
ajst-31195	65	34	mapping	mapping	NOUN
ajst-31195	65	35	of	of	ADP
ajst-31195	65	36	the	the	DET
ajst-31195	65	37	previous	previous	ADJ
ajst-31195	65	38	convolutional	convolutional	ADJ
ajst-31195	65	39	layer	layer	NOUN
ajst-31195	65	40	;	;	PUNCT
ajst-31195	65	41	𝑠	𝑠	PROPN
ajst-31195	65	42	denotes	denote	VERB
ajst-31195	65	43	the	the	DET
ajst-31195	65	44	output	output	NOUN
ajst-31195	65	45	feature	feature	NOUN
ajst-31195	65	46	range	range	NOUN
ajst-31195	65	47	of	of	ADP
ajst-31195	65	48	the	the	DET
ajst-31195	65	49	pooling	pool	VERB
ajst-31195	65	50	layer	layer	NOUN
ajst-31195	65	51	.	.	PUNCT
ajst-31195	66	1	3	3	X
ajst-31195	66	2	)	)	PUNCT
ajst-31195	66	3	full	full	ADJ
ajst-31195	66	4	connectivity	connectivity	NOUN
ajst-31195	66	5	layer	layer	NOUN
ajst-31195	66	6	the	the	DET
ajst-31195	66	7	fully	fully	ADV
ajst-31195	66	8	connected	connect	VERB
ajst-31195	66	9	layer	layer	NOUN
ajst-31195	66	10	plays	play	VERB
ajst-31195	66	11	a	a	DET
ajst-31195	66	12	role	role	NOUN
ajst-31195	66	13	in	in	ADP
ajst-31195	66	14	the	the	DET
ajst-31195	66	15	yolov8x	yolov8x	NOUN
ajst-31195	66	16	-	-	PUNCT
ajst-31195	66	17	seg	seg	PROPN
ajst-31195	66	18	model	model	NOUN
ajst-31195	66	19	by	by	ADP
ajst-31195	66	20	integrating	integrate	VERB
ajst-31195	66	21	the	the	DET
ajst-31195	66	22	extracted	extract	VERB
ajst-31195	66	23	features	feature	NOUN
ajst-31195	66	24	,	,	PUNCT
ajst-31195	66	25	nonlinear	nonlinear	ADJ
ajst-31195	66	26	mapping	mapping	NOUN
ajst-31195	66	27	,	,	PUNCT
ajst-31195	66	28	classification	classification	NOUN
ajst-31195	66	29	or	or	CCONJ
ajst-31195	66	30	prediction	prediction	NOUN
ajst-31195	66	31	,	,	PUNCT
ajst-31195	66	32	and	and	CCONJ
ajst-31195	66	33	parameter	parameter	NOUN
ajst-31195	66	34	learning	learning	NOUN
ajst-31195	66	35	and	and	CCONJ
ajst-31195	66	36	optimization	optimization	NOUN
ajst-31195	66	37	.	.	PUNCT
ajst-31195	67	1	through	through	ADP
ajst-31195	67	2	the	the	DET
ajst-31195	67	3	processing	processing	NOUN
ajst-31195	67	4	of	of	ADP
ajst-31195	67	5	the	the	DET
ajst-31195	67	6	fully	fully	ADV
ajst-31195	67	7	connected	connected	ADJ
ajst-31195	67	8	layer	layer	NOUN
ajst-31195	67	9	,	,	PUNCT
ajst-31195	67	10	yolov8x	yolov8x	NOUN
ajst-31195	67	11	-	-	PUNCT
ajst-31195	67	12	seg	seg	PROPN
ajst-31195	67	13	can	can	AUX
ajst-31195	67	14	transform	transform	VERB
ajst-31195	67	15	the	the	DET
ajst-31195	67	16	input	input	NOUN
ajst-31195	67	17	data	datum	NOUN
ajst-31195	67	18	into	into	ADP
ajst-31195	67	19	probability	probability	NOUN
ajst-31195	67	20	distributions	distribution	NOUN
ajst-31195	67	21	of	of	ADP
ajst-31195	67	22	the	the	DET
ajst-31195	67	23	output	output	NOUN
ajst-31195	67	24	categories	category	NOUN
ajst-31195	67	25	to	to	PART
ajst-31195	67	26	accomplish	accomplish	VERB
ajst-31195	67	27	specific	specific	ADJ
ajst-31195	67	28	tasks	task	NOUN
ajst-31195	67	29	such	such	ADJ
ajst-31195	67	30	as	as	ADP
ajst-31195	67	31	classification	classification	NOUN
ajst-31195	67	32	and	and	CCONJ
ajst-31195	67	33	prediction	prediction	NOUN
ajst-31195	67	34	.	.	PUNCT
ajst-31195	68	1	the	the	DET
ajst-31195	68	2	fully	fully	ADV
ajst-31195	68	3	connected	connect	VERB
ajst-31195	68	4	layer	layer	NOUN
ajst-31195	68	5	is	be	AUX
ajst-31195	68	6	to	to	PART
ajst-31195	68	7	expand	expand	VERB
ajst-31195	68	8	the	the	DET
ajst-31195	68	9	feature	feature	NOUN
ajst-31195	68	10	mapping	mapping	NOUN
ajst-31195	68	11	after	after	ADP
ajst-31195	68	12	a	a	DET
ajst-31195	68	13	number	number	NOUN
ajst-31195	68	14	of	of	ADP
ajst-31195	68	15	convolution	convolution	NOUN
ajst-31195	68	16	and	and	CCONJ
ajst-31195	68	17	pooling	pool	VERB
ajst-31195	68	18	operations	operation	NOUN
ajst-31195	68	19	in	in	ADP
ajst-31195	68	20	this	this	DET
ajst-31195	68	21	way	way	NOUN
ajst-31195	68	22	by	by	ADP
ajst-31195	68	23	rows	row	NOUN
ajst-31195	68	24	,	,	PUNCT
ajst-31195	68	25	connected	connect	VERB
ajst-31195	68	26	into	into	ADP
ajst-31195	68	27	a	a	DET
ajst-31195	68	28	one	one	NUM
ajst-31195	68	29	-	-	PUNCT
ajst-31195	68	30	dimensional	dimensional	ADJ
ajst-31195	68	31	vector	vector	NOUN
ajst-31195	68	32	,	,	PUNCT
ajst-31195	68	33	and	and	CCONJ
ajst-31195	68	34	then	then	ADV
ajst-31195	68	35	apply	apply	VERB
ajst-31195	68	36	the	the	DET
ajst-31195	68	37	softmax	softmax	NOUN
ajst-31195	68	38	function	function	NOUN
ajst-31195	68	39	to	to	PART
ajst-31195	68	40	obtain	obtain	VERB
ajst-31195	68	41	the	the	DET
ajst-31195	68	42	classification	classification	NOUN
ajst-31195	68	43	of	of	ADP
ajst-31195	68	44	the	the	DET
ajst-31195	68	45	five	five	NUM
ajst-31195	68	46	different	different	ADJ
ajst-31195	68	47	fruits	fruit	NOUN
ajst-31195	68	48	.	.	PUNCT
ajst-31195	69	1	the	the	DET
ajst-31195	69	2	softmax	softmax	NOUN
ajst-31195	69	3	expression	expression	NOUN
ajst-31195	69	4	is	be	AUX
ajst-31195	69	5	as	as	SCONJ
ajst-31195	69	6	follows	follow	VERB
ajst-31195	69	7	:	:	PUNCT
ajst-31195	69	8	𝑞	𝑞	X
ajst-31195	69	9	∑	∑	PROPN
ajst-31195	69	10	(	(	PUNCT
ajst-31195	69	11	3	3	NUM
ajst-31195	69	12	)	)	PUNCT
ajst-31195	69	13	where	where	SCONJ
ajst-31195	69	14	𝑞	𝑞	PROPN
ajst-31195	69	15	is	be	AUX
ajst-31195	69	16	the	the	DET
ajst-31195	69	17	classification	classification	NOUN
ajst-31195	69	18	output	output	NOUN
ajst-31195	69	19	result	result	NOUN
ajst-31195	69	20	of	of	ADP
ajst-31195	69	21	the	the	DET
ajst-31195	69	22	convolutional	convolutional	ADJ
ajst-31195	69	23	neural	neural	ADJ
ajst-31195	69	24	network	network	NOUN
ajst-31195	69	25	,	,	PUNCT
ajst-31195	69	26	and	and	CCONJ
ajst-31195	69	27	𝑧	𝑧	PROPN
ajst-31195	69	28	is	be	AUX
ajst-31195	69	29	the	the	DET
ajst-31195	69	30	logarithm	logarithm	NOUN
ajst-31195	69	31	of	of	ADP
ajst-31195	69	32	the	the	DET
ajst-31195	69	33	𝑗th	𝑗th	PROPN
ajst-31195	69	34	output	output	NOUN
ajst-31195	69	35	neuron[5	neuron[5	NOUN
ajst-31195	69	36	]	]	PUNCT
ajst-31195	69	37	.	.	PUNCT
ajst-31195	70	1	2.4	2.4	NUM
ajst-31195	70	2	.	.	PUNCT
ajst-31195	71	1	adam	adam	PROPN
ajst-31195	71	2	gradient	gradient	PROPN
ajst-31195	71	3	descent	descent	NOUN
ajst-31195	71	4	algorithm	algorithm	NOUN
ajst-31195	71	5	the	the	DET
ajst-31195	71	6	core	core	ADJ
ajst-31195	71	7	idea	idea	NOUN
ajst-31195	71	8	of	of	ADP
ajst-31195	71	9	the	the	DET
ajst-31195	71	10	adam	adam	PROPN
ajst-31195	71	11	gradient	gradient	PROPN
ajst-31195	71	12	descent	descent	NOUN
ajst-31195	71	13	algorithm	algorithm	NOUN
ajst-31195	71	14	is	be	AUX
ajst-31195	71	15	to	to	PART
ajst-31195	71	16	update	update	VERB
ajst-31195	71	17	the	the	DET
ajst-31195	71	18	parameters	parameter	NOUN
ajst-31195	71	19	based	base	VERB
ajst-31195	71	20	on	on	ADP
ajst-31195	71	21	the	the	DET
ajst-31195	71	22	gradient	gradient	NOUN
ajst-31195	71	23	of	of	ADP
ajst-31195	71	24	each	each	DET
ajst-31195	71	25	parameter	parameter	NOUN
ajst-31195	71	26	and	and	CCONJ
ajst-31195	71	27	maintain	maintain	VERB
ajst-31195	71	28	an	an	DET
ajst-31195	71	29	adaptive	adaptive	ADJ
ajst-31195	71	30	learning	learning	NOUN
ajst-31195	71	31	rate	rate	NOUN
ajst-31195	71	32	for	for	ADP
ajst-31195	71	33	each	each	DET
ajst-31195	71	34	parameter	parameter	NOUN
ajst-31195	71	35	.	.	PUNCT
ajst-31195	72	1	specifically	specifically	ADV
ajst-31195	72	2	,	,	PUNCT
ajst-31195	72	3	the	the	DET
ajst-31195	72	4	adam	adam	PROPN
ajst-31195	72	5	gradient	gradient	PROPN
ajst-31195	72	6	descent	descent	NOUN
ajst-31195	72	7	algorithm	algorithm	PROPN
ajst-31195	72	8	updates	update	VERB
ajst-31195	72	9	the	the	DET
ajst-31195	72	10	parameters	parameter	NOUN
ajst-31195	72	11	by	by	ADP
ajst-31195	72	12	computing	compute	VERB
ajst-31195	72	13	first	first	ADJ
ajst-31195	72	14	-	-	PUNCT
ajst-31195	72	15	order	order	NOUN
ajst-31195	72	16	moment	moment	NOUN
ajst-31195	72	17	estimates	estimate	NOUN
ajst-31195	72	18	and	and	CCONJ
ajst-31195	72	19	second	second	ADJ
ajst-31195	72	20	-	-	PUNCT
ajst-31195	72	21	order	order	NOUN
ajst-31195	72	22	moment	moment	NOUN
ajst-31195	72	23	estimates	estimate	NOUN
ajst-31195	72	24	of	of	ADP
ajst-31195	72	25	the	the	DET
ajst-31195	72	26	gradient	gradient	NOUN
ajst-31195	72	27	.	.	PUNCT
ajst-31195	73	1	the	the	DET
ajst-31195	73	2	firstorder	firstorder	NOUN
ajst-31195	73	3	moment	moment	NOUN
ajst-31195	73	4	estimate	estimate	NOUN
ajst-31195	73	5	uses	use	VERB
ajst-31195	73	6	an	an	DET
ajst-31195	73	7	exponentially	exponentially	ADV
ajst-31195	73	8	weighted	weight	VERB
ajst-31195	73	9	moving	move	VERB
ajst-31195	73	10	average	average	ADJ
ajst-31195	73	11	to	to	PART
ajst-31195	73	12	estimate	estimate	VERB
ajst-31195	73	13	the	the	DET
ajst-31195	73	14	expected	expect	VERB
ajst-31195	73	15	value	value	NOUN
ajst-31195	73	16	of	of	ADP
ajst-31195	73	17	the	the	DET
ajst-31195	73	18	gradient	gradient	NOUN
ajst-31195	73	19	,	,	PUNCT
ajst-31195	73	20	which	which	PRON
ajst-31195	73	21	reduces	reduce	VERB
ajst-31195	73	22	the	the	DET
ajst-31195	73	23	variance	variance	NOUN
ajst-31195	73	24	of	of	ADP
ajst-31195	73	25	the	the	DET
ajst-31195	73	26	gradient	gradient	NOUN
ajst-31195	73	27	,	,	PUNCT
ajst-31195	73	28	and	and	CCONJ
ajst-31195	73	29	the	the	DET
ajst-31195	73	30	secondorder	secondorder	ADJ
ajst-31195	73	31	moment	moment	NOUN
ajst-31195	73	32	estimate	estimate	NOUN
ajst-31195	73	33	uses	use	VERB
ajst-31195	73	34	an	an	DET
ajst-31195	73	35	exponentially	exponentially	ADV
ajst-31195	73	36	weighted	weight	VERB
ajst-31195	73	37	moving	move	VERB
ajst-31195	73	38	average	average	ADJ
ajst-31195	73	39	to	to	PART
ajst-31195	73	40	estimate	estimate	VERB
ajst-31195	73	41	the	the	DET
ajst-31195	73	42	variance	variance	NOUN
ajst-31195	73	43	of	of	ADP
ajst-31195	73	44	the	the	DET
ajst-31195	73	45	gradient	gradient	NOUN
ajst-31195	73	46	,	,	PUNCT
ajst-31195	73	47	which	which	PRON
ajst-31195	73	48	maintains	maintain	VERB
ajst-31195	73	49	the	the	DET
ajst-31195	73	50	stability	stability	NOUN
ajst-31195	73	51	of	of	ADP
ajst-31195	73	52	the	the	DET
ajst-31195	73	53	parameter	parameter	NOUN
ajst-31195	73	54	updates[6	updates[6	NOUN
ajst-31195	73	55	]	]	PUNCT
ajst-31195	73	56	.	.	PUNCT
ajst-31195	74	1	during	during	ADP
ajst-31195	74	2	each	each	DET
ajst-31195	74	3	parameter	parameter	NOUN
ajst-31195	74	4	update	update	NOUN
ajst-31195	74	5	,	,	PUNCT
ajst-31195	74	6	the	the	DET
ajst-31195	74	7	adam	adam	PROPN
ajst-31195	74	8	gradient	gradient	PROPN
ajst-31195	74	9	descent	descent	NOUN
ajst-31195	74	10	algorithm	algorithm	NOUN
ajst-31195	74	11	is	be	AUX
ajst-31195	74	12	updated	update	VERB
ajst-31195	74	13	with	with	ADP
ajst-31195	74	14	the	the	DET
ajst-31195	74	15	following	follow	VERB
ajst-31195	74	16	formula	formula	NOUN
ajst-31195	74	17	:	:	PUNCT
ajst-31195	74	18	𝑚	𝑚	ADP
ajst-31195	74	19	𝛽	𝛽	X
ajst-31195	74	20	⋅	⋅	X
ajst-31195	74	21	𝑚	𝑚	PROPN
ajst-31195	74	22	𝐼	𝐼	PROPN
ajst-31195	74	23	𝛽	𝛽	PROPN
ajst-31195	74	24	⋅	⋅	PROPN
ajst-31195	74	25	𝑔	𝑔	X
ajst-31195	74	26	(	(	PUNCT
ajst-31195	74	27	4	4	NUM
ajst-31195	74	28	)	)	PUNCT
ajst-31195	74	29	𝑣	𝑣	DET
ajst-31195	74	30	𝛽	𝛽	NOUN
ajst-31195	74	31	⋅	⋅	X
ajst-31195	74	32	𝑣	𝑣	PROPN
ajst-31195	74	33	𝐼	𝐼	PROPN
ajst-31195	74	34	𝛽	𝛽	PROPN
ajst-31195	74	35	⋅	⋅	PROPN
ajst-31195	74	36	𝑔	𝑔	PROPN
ajst-31195	74	37	(	(	PUNCT
ajst-31195	74	38	5	5	NUM
ajst-31195	74	39	)	)	PUNCT
ajst-31195	74	40	𝑚	𝑚	NOUN
ajst-31195	74	41	(	(	PUNCT
ajst-31195	74	42	6	6	NUM
ajst-31195	74	43	)	)	PUNCT
ajst-31195	74	44	𝑣	𝑣	NOUN
ajst-31195	74	45	(	(	PUNCT
ajst-31195	74	46	7	7	NUM
ajst-31195	74	47	)	)	PUNCT
ajst-31195	74	48	𝜃	𝜃	NOUN
ajst-31195	75	1	𝜃	𝜃	NOUN
ajst-31195	75	2	𝑚	𝑚	X
ajst-31195	75	3	`	`	PUNCT
ajst-31195	75	4	(	(	PUNCT
ajst-31195	75	5	8)	8)	NUM
ajst-31195	75	6	where	where	SCONJ
ajst-31195	75	7	𝑚	𝑚	PROPN
ajst-31195	75	8	represents	represent	VERB
ajst-31195	75	9	the	the	DET
ajst-31195	75	10	first	first	ADJ
ajst-31195	75	11	-	-	PUNCT
ajst-31195	75	12	order	order	NOUN
ajst-31195	75	13	moment	moment	NOUN
ajst-31195	75	14	estimate	estimate	NOUN
ajst-31195	75	15	of	of	ADP
ajst-31195	75	16	the	the	DET
ajst-31195	75	17	gradient	gradient	NOUN
ajst-31195	75	18	and	and	CCONJ
ajst-31195	75	19	𝑣	𝑣	PROPN
ajst-31195	75	20	represents	represent	VERB
ajst-31195	75	21	the	the	DET
ajst-31195	75	22	second	second	ADJ
ajst-31195	75	23	-	-	PUNCT
ajst-31195	75	24	order	order	NOUN
ajst-31195	75	25	moment	moment	NOUN
ajst-31195	75	26	estimate	estimate	NOUN
ajst-31195	75	27	of	of	ADP
ajst-31195	75	28	the	the	DET
ajst-31195	75	29	gradient	gradient	NOUN
ajst-31195	75	30	,	,	PUNCT
ajst-31195	75	31	and	and	CCONJ
ajst-31195	75	32	𝛽	𝛽	NOUN
ajst-31195	75	33	,	,	PUNCT
ajst-31195	75	34	𝛽	𝛽	PROPN
ajst-31195	75	35	is	be	AUX
ajst-31195	75	36	the	the	DET
ajst-31195	75	37	decay	decay	NOUN
ajst-31195	75	38	rate	rate	NOUN
ajst-31195	75	39	used	use	VERB
ajst-31195	75	40	to	to	PART
ajst-31195	75	41	control	control	VERB
ajst-31195	75	42	20	20	NUM
ajst-31195	75	43	the	the	DET
ajst-31195	75	44	first	first	ADJ
ajst-31195	75	45	-	-	PUNCT
ajst-31195	75	46	order	order	NOUN
ajst-31195	75	47	and	and	CCONJ
ajst-31195	75	48	second	second	ADJ
ajst-31195	75	49	-	-	PUNCT
ajst-31195	75	50	order	order	NOUN
ajst-31195	75	51	moment	moment	NOUN
ajst-31195	75	52	estimates	estimate	NOUN
ajst-31195	75	53	,	,	PUNCT
ajst-31195	75	54	𝑣	𝑣	PRON
ajst-31195	75	55	is	be	AUX
ajst-31195	75	56	the	the	DET
ajst-31195	75	57	learning	learning	NOUN
ajst-31195	75	58	rate	rate	NOUN
ajst-31195	75	59	,	,	PUNCT
ajst-31195	75	60	𝜖	𝜖	PROPN
ajst-31195	75	61	is	be	AUX
ajst-31195	75	62	a	a	DET
ajst-31195	75	63	very	very	ADV
ajst-31195	75	64	small	small	ADJ
ajst-31195	75	65	constant	constant	ADJ
ajst-31195	75	66	in	in	ADP
ajst-31195	75	67	order	order	NOUN
ajst-31195	75	68	to	to	PART
ajst-31195	75	69	avoid	avoid	VERB
ajst-31195	75	70	a	a	DET
ajst-31195	75	71	denominator	denominator	NOUN
ajst-31195	75	72	of	of	ADP
ajst-31195	75	73	0	0	NUM
ajst-31195	75	74	,	,	PUNCT
ajst-31195	75	75	and	and	CCONJ
ajst-31195	75	76	𝑡	𝑡	PROPN
ajst-31195	75	77	is	be	AUX
ajst-31195	75	78	the	the	DET
ajst-31195	75	79	number	number	NOUN
ajst-31195	75	80	of	of	ADP
ajst-31195	75	81	current	current	ADJ
ajst-31195	75	82	iterations	iteration	NOUN
ajst-31195	75	83	.	.	PUNCT
ajst-31195	76	1	finally	finally	ADV
ajst-31195	76	2	,	,	PUNCT
ajst-31195	76	3	the	the	DET
ajst-31195	76	4	𝜃	𝜃	NOUN
ajst-31195	76	5	represents	represent	VERB
ajst-31195	76	6	the	the	DET
ajst-31195	76	7	updated	update	VERB
ajst-31195	76	8	values	value	NOUN
ajst-31195	76	9	of	of	ADP
ajst-31195	76	10	the	the	DET
ajst-31195	76	11	parameters	parameter	NOUN
ajst-31195	76	12	.	.	PUNCT
ajst-31195	77	1	with	with	ADP
ajst-31195	77	2	these	these	DET
ajst-31195	77	3	formulas	formula	NOUN
ajst-31195	77	4	,	,	PUNCT
ajst-31195	77	5	adam	adam	PROPN
ajst-31195	77	6	's	's	PART
ajst-31195	77	7	algorithm	algorithm	NOUN
ajst-31195	77	8	can	can	AUX
ajst-31195	77	9	adaptively	adaptively	ADV
ajst-31195	77	10	calculate	calculate	VERB
ajst-31195	77	11	the	the	DET
ajst-31195	77	12	learning	learning	NOUN
ajst-31195	77	13	rate	rate	NOUN
ajst-31195	77	14	for	for	ADP
ajst-31195	77	15	each	each	DET
ajst-31195	77	16	parameter	parameter	NOUN
ajst-31195	77	17	and	and	CCONJ
ajst-31195	77	18	update	update	VERB
ajst-31195	77	19	the	the	DET
ajst-31195	77	20	parameters	parameter	NOUN
ajst-31195	77	21	based	base	VERB
ajst-31195	77	22	on	on	ADP
ajst-31195	77	23	historical	historical	ADJ
ajst-31195	77	24	gradient	gradient	NOUN
ajst-31195	77	25	information	information	NOUN
ajst-31195	77	26	.	.	PUNCT
ajst-31195	78	1	3	3	X
ajst-31195	78	2	.	.	X
ajst-31195	78	3	modeling	modeling	NOUN
ajst-31195	78	4	and	and	CCONJ
ajst-31195	78	5	solving	solve	VERB
ajst-31195	78	6	this	this	DET
ajst-31195	78	7	study	study	NOUN
ajst-31195	78	8	implemented	implement	VERB
ajst-31195	78	9	a	a	DET
ajst-31195	78	10	systematic	systematic	ADJ
ajst-31195	78	11	optimization	optimization	NOUN
ajst-31195	78	12	process	process	NOUN
ajst-31195	78	13	for	for	ADP
ajst-31195	78	14	the	the	DET
ajst-31195	78	15	yolov8x	yolov8x	PROPN
ajst-31195	78	16	-	-	PUNCT
ajst-31195	78	17	seg	seg	PROPN
ajst-31195	78	18	model	model	NOUN
ajst-31195	78	19	using	use	VERB
ajst-31195	78	20	the	the	DET
ajst-31195	78	21	adam	adam	PROPN
ajst-31195	78	22	gradient	gradient	ADJ
ajst-31195	78	23	descent	descent	NOUN
ajst-31195	78	24	algorithm	algorithm	NOUN
ajst-31195	78	25	.	.	PUNCT
ajst-31195	79	1	as	as	SCONJ
ajst-31195	79	2	illustrated	illustrate	VERB
ajst-31195	79	3	in	in	ADP
ajst-31195	79	4	figure	figure	NOUN
ajst-31195	79	5	3	3	NUM
ajst-31195	79	6	,	,	PUNCT
ajst-31195	79	7	the	the	DET
ajst-31195	79	8	complete	complete	ADJ
ajst-31195	79	9	workflow	workflow	NOUN
ajst-31195	79	10	encompasses	encompass	VERB
ajst-31195	79	11	data	datum	NOUN
ajst-31195	79	12	preprocessing	preprocessing	NOUN
ajst-31195	79	13	,	,	PUNCT
ajst-31195	79	14	model	model	NOUN
ajst-31195	79	15	configuration	configuration	NOUN
ajst-31195	79	16	,	,	PUNCT
ajst-31195	79	17	iterative	iterative	NOUN
ajst-31195	79	18	training	training	NOUN
ajst-31195	79	19	,	,	PUNCT
ajst-31195	79	20	and	and	CCONJ
ajst-31195	79	21	performance	performance	NOUN
ajst-31195	79	22	validation	validation	NOUN
ajst-31195	79	23	.	.	PUNCT
ajst-31195	80	1	critical	critical	ADJ
ajst-31195	80	2	hyperparameters	hyperparameter	NOUN
ajst-31195	80	3	were	be	AUX
ajst-31195	80	4	configured	configure	VERB
ajst-31195	80	5	as	as	SCONJ
ajst-31195	80	6	follows	follow	VERB
ajst-31195	80	7	:	:	PUNCT
ajst-31195	81	1	maximum	maximum	ADJ
ajst-31195	81	2	training	training	NOUN
ajst-31195	81	3	epochs	epoch	NOUN
ajst-31195	81	4	(	(	PUNCT
ajst-31195	81	5	1,000	1,000	NUM
ajst-31195	81	6	)	)	PUNCT
ajst-31195	81	7	,	,	PUNCT
ajst-31195	81	8	initial	initial	ADJ
ajst-31195	81	9	learning	learning	NOUN
ajst-31195	81	10	rate	rate	NOUN
ajst-31195	81	11	(	(	PUNCT
ajst-31195	81	12	0.001	0.001	NUM
ajst-31195	81	13	)	)	PUNCT
ajst-31195	81	14	,	,	PUNCT
ajst-31195	81	15	l2	l2	NOUN
ajst-31195	81	16	regularization	regularization	NOUN
ajst-31195	81	17	coefficient	coefficient	NOUN
ajst-31195	81	18	(	(	PUNCT
ajst-31195	81	19	λ=0.0001	λ=0.0001	PROPN
ajst-31195	81	20	)	)	PUNCT
ajst-31195	81	21	,	,	PUNCT
ajst-31195	81	22	and	and	CCONJ
ajst-31195	81	23	a	a	DET
ajst-31195	81	24	piecewise	piecewise	NOUN
ajst-31195	81	25	constant	constant	ADJ
ajst-31195	81	26	decay	decay	NOUN
ajst-31195	81	27	strategy	strategy	NOUN
ajst-31195	81	28	with	with	ADP
ajst-31195	81	29	decay	decay	NOUN
ajst-31195	81	30	factor	factor	NOUN
ajst-31195	81	31	0.1	0.1	NUM
ajst-31195	81	32	applied	apply	VERB
ajst-31195	81	33	every	every	DET
ajst-31195	81	34	500	500	NUM
ajst-31195	81	35	epochs	epoch	NOUN
ajst-31195	81	36	.	.	PUNCT
ajst-31195	82	1	these	these	DET
ajst-31195	82	2	settings	setting	NOUN
ajst-31195	82	3	effectively	effectively	ADV
ajst-31195	82	4	balanced	balance	VERB
ajst-31195	82	5	model	model	NOUN
ajst-31195	82	6	convergence	convergence	NOUN
ajst-31195	82	7	speed	speed	NOUN
ajst-31195	82	8	with	with	ADP
ajst-31195	82	9	generalization	generalization	NOUN
ajst-31195	82	10	capability	capability	NOUN
ajst-31195	82	11	while	while	SCONJ
ajst-31195	82	12	mitigating	mitigate	VERB
ajst-31195	82	13	overfitting	overfitte	VERB
ajst-31195	82	14	risks	risk	NOUN
ajst-31195	82	15	.	.	PUNCT
ajst-31195	83	1	1	1	X
ajst-31195	83	2	)	)	PUNCT
ajst-31195	83	3	training	training	NOUN
ajst-31195	83	4	environment	environment	NOUN
ajst-31195	83	5	configuration	configuration	NOUN
ajst-31195	83	6	the	the	DET
ajst-31195	83	7	experimental	experimental	ADJ
ajst-31195	83	8	setup	setup	NOUN
ajst-31195	83	9	involved	involve	VERB
ajst-31195	83	10	three	three	NUM
ajst-31195	83	11	distinct	distinct	ADJ
ajst-31195	83	12	datasets	dataset	NOUN
ajst-31195	83	13	:	:	PUNCT
ajst-31195	83	14	1	1	X
ajst-31195	83	15	)	)	PUNCT
ajst-31195	83	16	70	70	NUM
ajst-31195	83	17	%	%	NOUN
ajst-31195	83	18	of	of	ADP
ajst-31195	83	19	images	image	NOUN
ajst-31195	83	20	for	for	ADP
ajst-31195	83	21	model	model	NOUN
ajst-31195	83	22	training	training	NOUN
ajst-31195	83	23	,	,	PUNCT
ajst-31195	83	24	2	2	NUM
ajst-31195	83	25	)	)	PUNCT
ajst-31195	83	26	15	15	NUM
ajst-31195	83	27	%	%	NOUN
ajst-31195	83	28	for	for	ADP
ajst-31195	83	29	validationbased	validationbase	VERB
ajst-31195	83	30	hyperparameter	hyperparameter	NOUN
ajst-31195	83	31	tuning	tuning	NOUN
ajst-31195	83	32	,	,	PUNCT
ajst-31195	83	33	and	and	CCONJ
ajst-31195	83	34	3	3	X
ajst-31195	83	35	)	)	PUNCT
ajst-31195	83	36	15	15	NUM
ajst-31195	83	37	%	%	NOUN
ajst-31195	83	38	reserved	reserve	VERB
ajst-31195	83	39	for	for	ADP
ajst-31195	83	40	final	final	ADJ
ajst-31195	83	41	performance	performance	NOUN
ajst-31195	83	42	evaluation	evaluation	NOUN
ajst-31195	83	43	.	.	PUNCT
ajst-31195	84	1	as	as	SCONJ
ajst-31195	84	2	demonstrated	demonstrate	VERB
ajst-31195	84	3	in	in	ADP
ajst-31195	84	4	figure	figure	NOUN
ajst-31195	84	5	4	4	NUM
ajst-31195	84	6	,	,	PUNCT
ajst-31195	84	7	input	input	NOUN
ajst-31195	84	8	images	image	NOUN
ajst-31195	84	9	underwent	undergo	VERB
ajst-31195	84	10	standardized	standardized	ADJ
ajst-31195	84	11	preprocessing	preprocessing	NOUN
ajst-31195	84	12	including	include	VERB
ajst-31195	84	13	sharpening	sharpen	VERB
ajst-31195	84	14	and	and	CCONJ
ajst-31195	84	15	median	median	ADJ
ajst-31195	84	16	filtering	filtering	NOUN
ajst-31195	84	17	(	(	PUNCT
ajst-31195	84	18	section	section	NOUN
ajst-31195	84	19	2.2	2.2	NUM
ajst-31195	84	20	)	)	PUNCT
ajst-31195	84	21	,	,	PUNCT
ajst-31195	84	22	while	while	SCONJ
ajst-31195	84	23	output	output	NOUN
ajst-31195	84	24	layers	layer	NOUN
ajst-31195	84	25	generated	generate	VERB
ajst-31195	84	26	both	both	DET
ajst-31195	84	27	instance	instance	NOUN
ajst-31195	84	28	segmentation	segmentation	NOUN
ajst-31195	84	29	masks	mask	NOUN
ajst-31195	84	30	and	and	CCONJ
ajst-31195	84	31	quantitative	quantitative	ADJ
ajst-31195	84	32	statistics	statistic	NOUN
ajst-31195	84	33	.	.	PUNCT
ajst-31195	85	1	2	2	X
ajst-31195	85	2	)	)	PUNCT
ajst-31195	85	3	iterative	iterative	NOUN
ajst-31195	85	4	optimization	optimization	NOUN
ajst-31195	85	5	process	process	NOUN
ajst-31195	85	6	during	during	ADP
ajst-31195	85	7	training	training	NOUN
ajst-31195	85	8	iterations	iteration	NOUN
ajst-31195	85	9	,	,	PUNCT
ajst-31195	85	10	the	the	DET
ajst-31195	85	11	model	model	NOUN
ajst-31195	85	12	autonomously	autonomously	ADV
ajst-31195	85	13	computed	compute	VERB
ajst-31195	85	14	cross	cross	ADJ
ajst-31195	85	15	-	-	ADJ
ajst-31195	85	16	entropy	entropy	ADJ
ajst-31195	85	17	loss	loss	NOUN
ajst-31195	85	18	and	and	CCONJ
ajst-31195	85	19	mean	mean	ADJ
ajst-31195	85	20	average	average	ADJ
ajst-31195	85	21	precision	precision	NOUN
ajst-31195	85	22	(	(	PUNCT
ajst-31195	85	23	map	map	NOUN
ajst-31195	85	24	)	)	PUNCT
ajst-31195	85	25	metrics	metric	NOUN
ajst-31195	85	26	through	through	ADP
ajst-31195	85	27	backpropagation	backpropagation	NOUN
ajst-31195	85	28	.	.	PUNCT
ajst-31195	86	1	the	the	DET
ajst-31195	86	2	adam	adam	PROPN
ajst-31195	86	3	optimizer	optimizer	NOUN
ajst-31195	86	4	dynamically	dynamically	ADV
ajst-31195	86	5	adjusted	adjust	VERB
ajst-31195	86	6	first	first	ADJ
ajst-31195	86	7	-	-	PUNCT
ajst-31195	86	8	order	order	NOUN
ajst-31195	86	9	(	(	PUNCT
ajst-31195	86	10	β1=0.9	β1=0.9	PROPN
ajst-31195	86	11	)	)	PUNCT
ajst-31195	86	12	and	and	CCONJ
ajst-31195	86	13	second	second	ADJ
ajst-31195	86	14	-	-	PUNCT
ajst-31195	86	15	order	order	NOUN
ajst-31195	86	16	momentum	momentum	NOUN
ajst-31195	86	17	parameters	parameter	NOUN
ajst-31195	86	18	(	(	PUNCT
ajst-31195	86	19	β2=0.999	β2=0.999	NUM
ajst-31195	86	20	)	)	PUNCT
ajst-31195	86	21	with	with	ADP
ajst-31195	86	22	ε=1e-8	ε=1e-8	PROPN
ajst-31195	86	23	numerical	numerical	ADJ
ajst-31195	86	24	stability	stability	NOUN
ajst-31195	86	25	constant	constant	ADJ
ajst-31195	86	26	,	,	PUNCT
ajst-31195	86	27	achieving	achieve	VERB
ajst-31195	86	28	adaptive	adaptive	ADJ
ajst-31195	86	29	learning	learning	NOUN
ajst-31195	86	30	rate	rate	NOUN
ajst-31195	86	31	control	control	NOUN
ajst-31195	86	32	across	across	ADP
ajst-31195	86	33	parameter	parameter	NOUN
ajst-31195	86	34	dimensions	dimension	NOUN
ajst-31195	86	35	.	.	PUNCT
ajst-31195	87	1	validation	validation	NOUN
ajst-31195	87	2	-	-	PUNCT
ajst-31195	87	3	phase	phase	NOUN
ajst-31195	87	4	results	result	NOUN
ajst-31195	87	5	guided	guide	VERB
ajst-31195	87	6	two	two	NUM
ajst-31195	87	7	types	type	NOUN
ajst-31195	87	8	of	of	ADP
ajst-31195	87	9	adjustments	adjustment	NOUN
ajst-31195	87	10	:	:	PUNCT
ajst-31195	87	11	1	1	X
ajst-31195	87	12	)	)	PUNCT
ajst-31195	87	13	architectural	architectural	ADJ
ajst-31195	87	14	modifications	modification	NOUN
ajst-31195	87	15	to	to	PART
ajst-31195	87	16	feature	feature	VERB
ajst-31195	87	17	fusion	fusion	NOUN
ajst-31195	87	18	layers	layer	NOUN
ajst-31195	87	19	,	,	PUNCT
ajst-31195	87	20	and	and	CCONJ
ajst-31195	87	21	2	2	X
ajst-31195	87	22	)	)	PUNCT
ajst-31195	87	23	learning	learn	VERB
ajst-31195	87	24	rate	rate	NOUN
ajst-31195	87	25	annealing	annealing	NOUN
ajst-31195	87	26	when	when	SCONJ
ajst-31195	87	27	validation	validation	NOUN
ajst-31195	87	28	loss	loss	NOUN
ajst-31195	87	29	plateaued	plateaue	VERB
ajst-31195	87	30	.	.	PUNCT
ajst-31195	88	1	3	3	X
ajst-31195	88	2	)	)	PUNCT
ajst-31195	88	3	performance	performance	NOUN
ajst-31195	88	4	evaluation	evaluation	NOUN
ajst-31195	88	5	final	final	ADJ
ajst-31195	88	6	testing	testing	NOUN
ajst-31195	88	7	on	on	ADP
ajst-31195	88	8	the	the	DET
ajst-31195	88	9	reserved	reserved	ADJ
ajst-31195	88	10	dataset	dataset	NOUN
ajst-31195	88	11	revealed	reveal	VERB
ajst-31195	88	12	three	three	NUM
ajst-31195	88	13	key	key	ADJ
ajst-31195	88	14	outcomes	outcome	NOUN
ajst-31195	88	15	:	:	PUNCT
ajst-31195	88	16	1	1	X
ajst-31195	88	17	)	)	PUNCT
ajst-31195	88	18	segmentation	segmentation	NOUN
ajst-31195	88	19	accuracy	accuracy	NOUN
ajst-31195	88	20	reached	reach	VERB
ajst-31195	88	21	96.2	96.2	NUM
ajst-31195	88	22	%	%	NOUN
ajst-31195	88	23	under	under	ADP
ajst-31195	88	24	occlusion	occlusion	NOUN
ajst-31195	88	25	conditions	condition	NOUN
ajst-31195	88	26	,	,	PUNCT
ajst-31195	88	27	2	2	X
ajst-31195	88	28	)	)	PUNCT
ajst-31195	88	29	inference	inference	NOUN
ajst-31195	88	30	speed	speed	NOUN
ajst-31195	88	31	maintained	maintain	VERB
ajst-31195	88	32	28	28	NUM
ajst-31195	88	33	fps	fps	NOUN
ajst-31195	88	34	on	on	ADP
ajst-31195	88	35	nvidia	nvidia	PROPN
ajst-31195	88	36	jetson	jetson	PROPN
ajst-31195	88	37	agx	agx	PROPN
ajst-31195	88	38	xavier	xavier	PROPN
ajst-31195	88	39	,	,	PUNCT
ajst-31195	88	40	and	and	CCONJ
ajst-31195	88	41	3	3	X
ajst-31195	88	42	)	)	PUNCT
ajst-31195	88	43	quantitative	quantitative	ADJ
ajst-31195	88	44	counting	counting	NOUN
ajst-31195	88	45	error	error	NOUN
ajst-31195	88	46	remained	remain	VERB
ajst-31195	88	47	below	below	ADP
ajst-31195	88	48	3	3	NUM
ajst-31195	88	49	%	%	NOUN
ajst-31195	88	50	in	in	ADP
ajst-31195	88	51	dense	dense	ADJ
ajst-31195	88	52	clusters	cluster	NOUN
ajst-31195	88	53	(	(	PUNCT
ajst-31195	88	54	figure	figure	NOUN
ajst-31195	88	55	5	5	NUM
ajst-31195	88	56	)	)	PUNCT
ajst-31195	88	57	.	.	PUNCT
ajst-31195	89	1	these	these	DET
ajst-31195	89	2	metrics	metric	NOUN
ajst-31195	89	3	confirm	confirm	VERB
ajst-31195	89	4	the	the	DET
ajst-31195	89	5	effectiveness	effectiveness	NOUN
ajst-31195	89	6	of	of	ADP
ajst-31195	89	7	our	our	PRON
ajst-31195	89	8	optimization	optimization	NOUN
ajst-31195	89	9	strategy	strategy	NOUN
ajst-31195	89	10	in	in	ADP
ajst-31195	89	11	addressing	address	VERB
ajst-31195	89	12	orchard	orchard	ADJ
ajst-31195	89	13	environment	environment	NOUN
ajst-31195	89	14	challenges	challenge	NOUN
ajst-31195	89	15	identified	identify	VERB
ajst-31195	89	16	in	in	ADP
ajst-31195	89	17	section	section	NOUN
ajst-31195	89	18	1	1	NUM
ajst-31195	89	19	.	.	PUNCT
ajst-31195	89	20	figure	figure	VERB
ajst-31195	89	21	3	3	NUM
ajst-31195	89	22	.	.	PUNCT
ajst-31195	89	23	image	image	NOUN
ajst-31195	89	24	processing	processing	NOUN
ajst-31195	89	25	flow	flow	NOUN
ajst-31195	89	26	chart	chart	NOUN
ajst-31195	89	27	figure	figure	NOUN
ajst-31195	89	28	4	4	NUM
ajst-31195	89	29	.	.	PUNCT
ajst-31195	89	30	input	input	NOUN
ajst-31195	89	31	and	and	CCONJ
ajst-31195	89	32	output	output	NOUN
ajst-31195	89	33	image	image	NOUN
ajst-31195	89	34	the	the	DET
ajst-31195	89	35	collected	collect	VERB
ajst-31195	89	36	apple	apple	NOUN
ajst-31195	89	37	data	datum	NOUN
ajst-31195	89	38	was	be	AUX
ajst-31195	89	39	organized	organize	VERB
ajst-31195	89	40	into	into	ADP
ajst-31195	89	41	a	a	DET
ajst-31195	89	42	format	format	NOUN
ajst-31195	89	43	suitable	suitable	ADJ
ajst-31195	89	44	for	for	ADP
ajst-31195	89	45	histogram	histogram	NOUN
ajst-31195	89	46	plotting	plotting	NOUN
ajst-31195	89	47	.	.	PUNCT
ajst-31195	90	1	the	the	DET
ajst-31195	90	2	vertical	vertical	ADJ
ajst-31195	90	3	axis	axis	NOUN
ajst-31195	90	4	represented	represent	VERB
ajst-31195	90	5	the	the	DET
ajst-31195	90	6	quantity	quantity	NOUN
ajst-31195	90	7	,	,	PUNCT
ajst-31195	90	8	while	while	SCONJ
ajst-31195	90	9	the	the	DET
ajst-31195	90	10	horizontal	horizontal	ADJ
ajst-31195	90	11	axis	axis	NOUN
ajst-31195	90	12	consisted	consist	VERB
ajst-31195	90	13	of	of	ADP
ajst-31195	90	14	different	different	ADJ
ajst-31195	90	15	intervals	interval	NOUN
ajst-31195	90	16	.	.	PUNCT
ajst-31195	91	1	a	a	DET
ajst-31195	91	2	data	data	NOUN
ajst-31195	91	3	visualization	visualization	NOUN
ajst-31195	91	4	tool	tool	NOUN
ajst-31195	91	5	and	and	CCONJ
ajst-31195	91	6	the	the	DET
ajst-31195	91	7	python	python	NOUN
ajst-31195	91	8	's	's	PART
ajst-31195	91	9	matplotlib	matplotlib	PROPN
ajst-31195	91	10	library	library	NOUN
ajst-31195	91	11	were	be	AUX
ajst-31195	91	12	utilized	utilize	VERB
ajst-31195	91	13	to	to	PART
ajst-31195	91	14	create	create	VERB
ajst-31195	91	15	the	the	DET
ajst-31195	91	16	histograms	histogram	NOUN
ajst-31195	91	17	.	.	PUNCT
ajst-31195	92	1	in	in	ADP
ajst-31195	92	2	each	each	DET
ajst-31195	92	3	histogram	histogram	NOUN
ajst-31195	92	4	,	,	PUNCT
ajst-31195	92	5	one	one	NUM
ajst-31195	92	6	bar	bar	NOUN
ajst-31195	92	7	corresponded	correspond	VERB
ajst-31195	92	8	to	to	ADP
ajst-31195	92	9	the	the	DET
ajst-31195	92	10	data	datum	NOUN
ajst-31195	92	11	of	of	ADP
ajst-31195	92	12	one	one	NUM
ajst-31195	92	13	apple	apple	NOUN
ajst-31195	92	14	image	image	NOUN
ajst-31195	92	15	,	,	PUNCT
ajst-31195	92	16	and	and	CCONJ
ajst-31195	92	17	the	the	DET
ajst-31195	92	18	height	height	NOUN
ajst-31195	92	19	of	of	ADP
ajst-31195	92	20	the	the	DET
ajst-31195	92	21	bar	bar	NOUN
ajst-31195	92	22	denoted	denote	VERB
ajst-31195	92	23	the	the	DET
ajst-31195	92	24	quantity	quantity	NOUN
ajst-31195	92	25	.	.	PUNCT
ajst-31195	93	1	after	after	ADP
ajst-31195	93	2	plotting	plot	VERB
ajst-31195	93	3	,	,	PUNCT
ajst-31195	93	4	appropriate	appropriate	ADJ
ajst-31195	93	5	labels	label	NOUN
ajst-31195	93	6	for	for	ADP
ajst-31195	93	7	both	both	CCONJ
ajst-31195	93	8	the	the	DET
ajst-31195	93	9	horizontal	horizontal	ADJ
ajst-31195	93	10	and	and	CCONJ
ajst-31195	93	11	vertical	vertical	ADJ
ajst-31195	93	12	axes	axis	NOUN
ajst-31195	93	13	,	,	PUNCT
ajst-31195	93	14	as	as	ADV
ajst-31195	93	15	well	well	ADV
ajst-31195	93	16	as	as	ADP
ajst-31195	93	17	captions	caption	NOUN
ajst-31195	93	18	,	,	PUNCT
ajst-31195	93	19	were	be	AUX
ajst-31195	93	20	added	add	VERB
ajst-31195	93	21	.	.	PUNCT
ajst-31195	94	1	subsequently	subsequently	ADV
ajst-31195	94	2	,	,	PUNCT
ajst-31195	94	3	the	the	DET
ajst-31195	94	4	plotted	plot	VERB
ajst-31195	94	5	histogram	histogram	NOUN
ajst-31195	94	6	was	be	AUX
ajst-31195	94	7	analyzed	analyze	VERB
ajst-31195	94	8	and	and	CCONJ
ajst-31195	94	9	interpreted	interpret	VERB
ajst-31195	94	10	.	.	PUNCT
ajst-31195	95	1	through	through	ADP
ajst-31195	95	2	this	this	DET
ajst-31195	95	3	histogram	histogram	NOUN
ajst-31195	95	4	,	,	PUNCT
ajst-31195	95	5	the	the	DET
ajst-31195	95	6	number	number	NOUN
ajst-31195	95	7	of	of	ADP
ajst-31195	95	8	apples	apple	NOUN
ajst-31195	95	9	in	in	ADP
ajst-31195	95	10	the	the	DET
ajst-31195	95	11	dataset	dataset	NOUN
ajst-31195	95	12	under	under	ADP
ajst-31195	95	13	study	study	NOUN
ajst-31195	95	14	could	could	AUX
ajst-31195	95	15	be	be	AUX
ajst-31195	95	16	clearly	clearly	ADV
ajst-31195	95	17	observed	observe	VERB
ajst-31195	95	18	.	.	PUNCT
ajst-31195	96	1	the	the	DET
ajst-31195	96	2	resulting	result	VERB
ajst-31195	96	3	histogram	histogram	NOUN
ajst-31195	96	4	of	of	ADP
ajst-31195	96	5	the	the	DET
ajst-31195	96	6	apple	apple	NOUN
ajst-31195	96	7	distribution	distribution	NOUN
ajst-31195	96	8	is	be	AUX
ajst-31195	96	9	shown	show	VERB
ajst-31195	96	10	in	in	ADP
ajst-31195	96	11	figure	figure	NOUN
ajst-31195	96	12	5	5	NUM
ajst-31195	96	13	:	:	SYM
ajst-31195	96	14	21	21	NUM
ajst-31195	96	15	figure	figure	NOUN
ajst-31195	96	16	5	5	NUM
ajst-31195	96	17	.	.	PUNCT
ajst-31195	96	18	histogram	histogram	NOUN
ajst-31195	96	19	of	of	ADP
ajst-31195	96	20	the	the	DET
ajst-31195	96	21	distribution	distribution	NOUN
ajst-31195	96	22	of	of	ADP
ajst-31195	96	23	apples	apple	NOUN
ajst-31195	96	24	as	as	SCONJ
ajst-31195	96	25	can	can	AUX
ajst-31195	96	26	be	be	AUX
ajst-31195	96	27	seen	see	VERB
ajst-31195	96	28	from	from	ADP
ajst-31195	96	29	the	the	DET
ajst-31195	96	30	figure	figure	NOUN
ajst-31195	96	31	,	,	PUNCT
ajst-31195	96	32	the	the	DET
ajst-31195	96	33	number	number	NOUN
ajst-31195	96	34	of	of	ADP
ajst-31195	96	35	apples	apple	NOUN
ajst-31195	96	36	varies	vary	VERB
ajst-31195	96	37	greatly	greatly	ADV
ajst-31195	96	38	from	from	ADP
ajst-31195	96	39	image	image	NOUN
ajst-31195	96	40	to	to	ADP
ajst-31195	96	41	image	image	NOUN
ajst-31195	96	42	.	.	PUNCT
ajst-31195	97	1	some	some	DET
ajst-31195	97	2	images	image	NOUN
ajst-31195	97	3	have	have	VERB
ajst-31195	97	4	very	very	ADV
ajst-31195	97	5	few	few	ADJ
ajst-31195	97	6	apples	apple	NOUN
ajst-31195	97	7	,	,	PUNCT
ajst-31195	97	8	close	close	ADJ
ajst-31195	97	9	to	to	ADP
ajst-31195	97	10	zero	zero	NUM
ajst-31195	97	11	,	,	PUNCT
ajst-31195	97	12	while	while	SCONJ
ajst-31195	97	13	others	other	NOUN
ajst-31195	97	14	have	have	VERB
ajst-31195	97	15	more	more	ADJ
ajst-31195	97	16	apples	apple	NOUN
ajst-31195	97	17	,	,	PUNCT
ajst-31195	97	18	close	close	ADJ
ajst-31195	97	19	to	to	ADP
ajst-31195	97	20	more	more	ADJ
ajst-31195	97	21	than	than	ADP
ajst-31195	97	22	20	20	NUM
ajst-31195	97	23	,	,	PUNCT
ajst-31195	97	24	and	and	CCONJ
ajst-31195	97	25	the	the	DET
ajst-31195	97	26	distribution	distribution	NOUN
ajst-31195	97	27	of	of	ADP
ajst-31195	97	28	apples	apple	NOUN
ajst-31195	97	29	in	in	ADP
ajst-31195	97	30	each	each	PRON
ajst-31195	97	31	of	of	ADP
ajst-31195	97	32	the	the	DET
ajst-31195	97	33	200	200	NUM
ajst-31195	97	34	images	image	NOUN
ajst-31195	97	35	is	be	AUX
ajst-31195	97	36	more	more	ADV
ajst-31195	97	37	dispersed	disperse	VERB
ajst-31195	97	38	.	.	PUNCT
ajst-31195	98	1	4	4	X
ajst-31195	98	2	.	.	X
ajst-31195	98	3	conclusions	conclusion	NOUN
ajst-31195	98	4	based	base	VERB
ajst-31195	98	5	on	on	ADP
ajst-31195	98	6	the	the	DET
ajst-31195	98	7	yolov8x	yolov8x	NOUN
ajst-31195	98	8	-	-	PUNCT
ajst-31195	98	9	seg	seg	PROPN
ajst-31195	98	10	detection	detection	NOUN
ajst-31195	98	11	model	model	NOUN
ajst-31195	98	12	of	of	ADP
ajst-31195	98	13	apple	apple	NOUN
ajst-31195	98	14	image	image	NOUN
ajst-31195	98	15	instance	instance	NOUN
ajst-31195	98	16	segmentation	segmentation	NOUN
ajst-31195	98	17	technology	technology	NOUN
ajst-31195	98	18	research	research	NOUN
ajst-31195	98	19	,	,	PUNCT
ajst-31195	98	20	through	through	ADP
ajst-31195	98	21	the	the	DET
ajst-31195	98	22	image	image	NOUN
ajst-31195	98	23	sharpening	sharpening	NOUN
ajst-31195	98	24	and	and	CCONJ
ajst-31195	98	25	median	median	ADJ
ajst-31195	98	26	filtering	filtering	NOUN
ajst-31195	98	27	preprocessing	preprocessing	NOUN
ajst-31195	98	28	combination	combination	NOUN
ajst-31195	98	29	effectively	effectively	ADV
ajst-31195	98	30	enhance	enhance	VERB
ajst-31195	98	31	the	the	DET
ajst-31195	98	32	edge	edge	NOUN
ajst-31195	98	33	features	feature	NOUN
ajst-31195	98	34	and	and	CCONJ
ajst-31195	98	35	inhibit	inhibit	VERB
ajst-31195	98	36	the	the	DET
ajst-31195	98	37	noise	noise	NOUN
ajst-31195	98	38	interference	interference	NOUN
ajst-31195	98	39	(	(	PUNCT
ajst-31195	98	40	the	the	DET
ajst-31195	98	41	average	average	ADJ
ajst-31195	98	42	noise	noise	NOUN
ajst-31195	98	43	suppression	suppression	NOUN
ajst-31195	98	44	rate	rate	NOUN
ajst-31195	98	45	increased	increase	VERB
ajst-31195	98	46	by	by	ADP
ajst-31195	98	47	about	about	ADV
ajst-31195	98	48	35	35	NUM
ajst-31195	98	49	%	%	NOUN
ajst-31195	98	50	)	)	PUNCT
ajst-31195	98	51	,	,	PUNCT
ajst-31195	98	52	combined	combine	VERB
ajst-31195	98	53	with	with	ADP
ajst-31195	98	54	the	the	DET
ajst-31195	98	55	adam	adam	PROPN
ajst-31195	98	56	gradient	gradient	PROPN
ajst-31195	98	57	descent	descent	NOUN
ajst-31195	98	58	algorithm	algorithm	NOUN
ajst-31195	98	59	to	to	PART
ajst-31195	98	60	optimize	optimize	VERB
ajst-31195	98	61	the	the	DET
ajst-31195	98	62	model	model	NOUN
ajst-31195	98	63	parameters	parameter	NOUN
ajst-31195	98	64	(	(	PUNCT
ajst-31195	98	65	the	the	DET
ajst-31195	98	66	learning	learning	NOUN
ajst-31195	98	67	rate	rate	NOUN
ajst-31195	98	68	of	of	ADP
ajst-31195	98	69	0.001	0.001	NUM
ajst-31195	98	70	,	,	PUNCT
ajst-31195	98	71	the	the	DET
ajst-31195	98	72	l2	l2	NOUN
ajst-31195	98	73	regularization	regularization	NOUN
ajst-31195	98	74	of	of	ADP
ajst-31195	98	75	0.0001	0.0001	NUM
ajst-31195	98	76	)	)	PUNCT
ajst-31195	98	77	,	,	PUNCT
ajst-31195	98	78	in	in	ADP
ajst-31195	98	79	200	200	NUM
ajst-31195	98	80	orchards	orchard	NOUN
ajst-31195	98	81	the	the	DET
ajst-31195	98	82	average	average	ADJ
ajst-31195	98	83	detection	detection	NOUN
ajst-31195	98	84	accuracy	accuracy	NOUN
ajst-31195	98	85	of	of	ADP
ajst-31195	98	86	95.2	95.2	NUM
ajst-31195	98	87	%	%	NOUN
ajst-31195	98	88	is	be	AUX
ajst-31195	98	89	achieved	achieve	VERB
ajst-31195	98	90	in	in	ADP
ajst-31195	98	91	the	the	DET
ajst-31195	98	92	real	real	ADJ
ajst-31195	98	93	images	image	NOUN
ajst-31195	98	94	,	,	PUNCT
ajst-31195	98	95	which	which	PRON
ajst-31195	98	96	is	be	AUX
ajst-31195	98	97	more	more	ADJ
ajst-31195	98	98	than	than	ADP
ajst-31195	98	99	15	15	NUM
ajst-31195	98	100	percentage	percentage	NOUN
ajst-31195	98	101	points	point	NOUN
ajst-31195	98	102	higher	high	ADJ
ajst-31195	98	103	than	than	ADP
ajst-31195	98	104	the	the	DET
ajst-31195	98	105	traditional	traditional	ADJ
ajst-31195	98	106	method	method	NOUN
ajst-31195	98	107	.	.	PUNCT
ajst-31195	99	1	with	with	ADP
ajst-31195	99	2	multi	multi	ADJ
ajst-31195	99	3	-	-	ADJ
ajst-31195	99	4	scale	scale	ADJ
ajst-31195	99	5	convolutional	convolutional	ADJ
ajst-31195	99	6	feature	feature	NOUN
ajst-31195	99	7	fusion	fusion	NOUN
ajst-31195	99	8	and	and	CCONJ
ajst-31195	99	9	dynamic	dynamic	ADJ
ajst-31195	99	10	pooling	pooling	NOUN
ajst-31195	99	11	strategy	strategy	NOUN
ajst-31195	99	12	,	,	PUNCT
ajst-31195	99	13	the	the	DET
ajst-31195	99	14	model	model	NOUN
ajst-31195	99	15	significantly	significantly	ADV
ajst-31195	99	16	improves	improve	VERB
ajst-31195	99	17	the	the	DET
ajst-31195	99	18	robustness	robustness	NOUN
ajst-31195	99	19	of	of	ADP
ajst-31195	99	20	recognition	recognition	NOUN
ajst-31195	99	21	of	of	ADP
ajst-31195	99	22	dense	dense	ADJ
ajst-31195	99	23	apples	apple	NOUN
ajst-31195	99	24	(	(	PUNCT
ajst-31195	99	25	maximum	maximum	ADJ
ajst-31195	99	26	detection	detection	NOUN
ajst-31195	99	27	of	of	ADP
ajst-31195	99	28	more	more	ADJ
ajst-31195	99	29	than	than	ADP
ajst-31195	99	30	20	20	NUM
ajst-31195	99	31	apples	apple	NOUN
ajst-31195	99	32	in	in	ADP
ajst-31195	99	33	a	a	DET
ajst-31195	99	34	single	single	ADJ
ajst-31195	99	35	image	image	NOUN
ajst-31195	99	36	)	)	PUNCT
ajst-31195	99	37	in	in	ADP
ajst-31195	99	38	complex	complex	ADJ
ajst-31195	99	39	scenes	scene	NOUN
ajst-31195	99	40	,	,	PUNCT
ajst-31195	99	41	and	and	CCONJ
ajst-31195	99	42	at	at	ADP
ajst-31195	99	43	the	the	DET
ajst-31195	99	44	same	same	ADJ
ajst-31195	99	45	time	time	NOUN
ajst-31195	99	46	increases	increase	VERB
ajst-31195	99	47	the	the	DET
ajst-31195	99	48	inference	inference	NOUN
ajst-31195	99	49	speed	speed	NOUN
ajst-31195	99	50	up	up	ADP
ajst-31195	99	51	to	to	PART
ajst-31195	99	52	28	28	NUM
ajst-31195	99	53	fps	fps	PROPN
ajst-31195	99	54	,	,	PUNCT
ajst-31195	99	55	which	which	PRON
ajst-31195	99	56	meets	meet	VERB
ajst-31195	99	57	the	the	DET
ajst-31195	99	58	demand	demand	NOUN
ajst-31195	99	59	of	of	ADP
ajst-31195	99	60	real	real	ADJ
ajst-31195	99	61	-	-	PUNCT
ajst-31195	99	62	time	time	NOUN
ajst-31195	99	63	operation	operation	NOUN
ajst-31195	99	64	in	in	ADP
ajst-31195	99	65	orchards	orchard	NOUN
ajst-31195	99	66	.	.	PUNCT
ajst-31195	100	1	the	the	DET
ajst-31195	100	2	study	study	NOUN
ajst-31195	100	3	validated	validate	VERB
ajst-31195	100	4	the	the	DET
ajst-31195	100	5	strong	strong	ADJ
ajst-31195	100	6	adaptability	adaptability	NOUN
ajst-31195	100	7	of	of	ADP
ajst-31195	100	8	yolov8x	yolov8x	NOUN
ajst-31195	100	9	-	-	PUNCT
ajst-31195	100	10	seg	seg	PROPN
ajst-31195	100	11	in	in	ADP
ajst-31195	100	12	branch	branch	NOUN
ajst-31195	100	13	and	and	CCONJ
ajst-31195	100	14	leaf	leaf	NOUN
ajst-31195	100	15	backgrounds	background	NOUN
ajst-31195	100	16	with	with	ADP
ajst-31195	100	17	only	only	ADV
ajst-31195	100	18	15	15	NUM
ajst-31195	100	19	%	%	NOUN
ajst-31195	100	20	hsv	hsv	NOUN
ajst-31195	100	21	color	color	NOUN
ajst-31195	100	22	difference	difference	NOUN
ajst-31195	100	23	,	,	PUNCT
ajst-31195	100	24	and	and	CCONJ
ajst-31195	100	25	enhanced	enhance	VERB
ajst-31195	100	26	the	the	DET
ajst-31195	100	27	model	model	NOUN
ajst-31195	100	28	generalization	generalization	NOUN
ajst-31195	100	29	capability	capability	NOUN
ajst-31195	100	30	by	by	ADP
ajst-31195	100	31	constructing	construct	VERB
ajst-31195	100	32	a	a	DET
ajst-31195	100	33	standardized	standardized	ADJ
ajst-31195	100	34	image	image	NOUN
ajst-31195	100	35	database	database	NOUN
ajst-31195	100	36	with	with	ADP
ajst-31195	100	37	multiproduction	multiproduction	NOUN
ajst-31195	100	38	area	area	NOUN
ajst-31195	100	39	,	,	PUNCT
ajst-31195	100	40	growing	grow	VERB
ajst-31195	100	41	period	period	NOUN
ajst-31195	100	42	and	and	CCONJ
ajst-31195	100	43	weather	weather	NOUN
ajst-31195	100	44	condition	condition	NOUN
ajst-31195	100	45	data	datum	NOUN
ajst-31195	100	46	.	.	PUNCT
ajst-31195	101	1	in	in	ADP
ajst-31195	101	2	the	the	DET
ajst-31195	101	3	future	future	NOUN
ajst-31195	101	4	,	,	PUNCT
ajst-31195	101	5	we	we	PRON
ajst-31195	101	6	will	will	AUX
ajst-31195	101	7	integrate	integrate	VERB
ajst-31195	101	8	millimeter	millimeter	NOUN
ajst-31195	101	9	-	-	PUNCT
ajst-31195	101	10	wave	wave	NOUN
ajst-31195	101	11	radar	radar	NOUN
ajst-31195	101	12	and	and	CCONJ
ajst-31195	101	13	multispectral	multispectral	ADJ
ajst-31195	101	14	imaging	imaging	NOUN
ajst-31195	101	15	to	to	PART
ajst-31195	101	16	achieve	achieve	VERB
ajst-31195	101	17	3d	3d	NUM
ajst-31195	101	18	spatial	spatial	ADJ
ajst-31195	101	19	localization	localization	NOUN
ajst-31195	101	20	,	,	PUNCT
ajst-31195	101	21	develop	develop	VERB
ajst-31195	101	22	a	a	DET
ajst-31195	101	23	lightweight	lightweight	ADJ
ajst-31195	101	24	version	version	NOUN
ajst-31195	101	25	adapted	adapt	VERB
ajst-31195	101	26	to	to	ADP
ajst-31195	101	27	embedded	embed	VERB
ajst-31195	101	28	hardware	hardware	NOUN
ajst-31195	101	29	with	with	ADP
ajst-31195	101	30	a	a	DET
ajst-31195	101	31	40	40	NUM
ajst-31195	101	32	%	%	NOUN
ajst-31195	101	33	compression	compression	NOUN
ajst-31195	101	34	of	of	ADP
ajst-31195	101	35	the	the	DET
ajst-31195	101	36	number	number	NOUN
ajst-31195	101	37	of	of	ADP
ajst-31195	101	38	parameters	parameter	NOUN
ajst-31195	101	39	,	,	PUNCT
ajst-31195	101	40	and	and	CCONJ
ajst-31195	101	41	establish	establish	VERB
ajst-31195	101	42	a	a	DET
ajst-31195	101	43	cross	cross	ADJ
ajst-31195	101	44	-	-	ADJ
ajst-31195	101	45	species	species	ADJ
ajst-31195	101	46	fruit	fruit	NOUN
ajst-31195	101	47	recognition	recognition	NOUN
ajst-31195	101	48	framework	framework	NOUN
ajst-31195	101	49	based	base	VERB
ajst-31195	101	50	on	on	ADP
ajst-31195	101	51	migration	migration	NOUN
ajst-31195	101	52	learning	learn	VERB
ajst-31195	101	53	to	to	PART
ajst-31195	101	54	provide	provide	VERB
ajst-31195	101	55	core	core	NOUN
ajst-31195	101	56	technology	technology	NOUN
ajst-31195	101	57	support	support	NOUN
ajst-31195	101	58	for	for	ADP
ajst-31195	101	59	an	an	DET
ajst-31195	101	60	all	all	DET
ajst-31195	101	61	-	-	PUNCT
ajst-31195	101	62	weather	weather	NOUN
ajst-31195	101	63	automated	automate	VERB
ajst-31195	101	64	harvesting	harvesting	NOUN
ajst-31195	101	65	system	system	NOUN
ajst-31195	101	66	.	.	PUNCT
ajst-31195	102	1	references	reference	NOUN
ajst-31195	102	2	[	[	X
ajst-31195	102	3	1	1	NUM
ajst-31195	102	4	]	]	X
ajst-31195	102	5	bai	bai	PROPN
ajst-31195	102	6	y	y	PROPN
ajst-31195	102	7	,	,	PUNCT
ajst-31195	102	8	zhang	zhang	PROPN
ajst-31195	102	9	b	b	PROPN
ajst-31195	102	10	,	,	PUNCT
ajst-31195	102	11	xu	xu	PROPN
ajst-31195	102	12	n	n	CCONJ
ajst-31195	102	13	,	,	PUNCT
ajst-31195	102	14	et	et	PROPN
ajst-31195	102	15	al	al	PROPN
ajst-31195	102	16	.	.	PUNCT
ajst-31195	102	17	vision	vision	NOUN
ajst-31195	102	18	-	-	PUNCT
ajst-31195	102	19	based	base	VERB
ajst-31195	102	20	navigation	navigation	NOUN
ajst-31195	102	21	and	and	CCONJ
ajst-31195	102	22	guidance	guidance	NOUN
ajst-31195	102	23	for	for	ADP
ajst-31195	102	24	agricultural	agricultural	ADJ
ajst-31195	102	25	autonomous	autonomous	ADJ
ajst-31195	102	26	vehicles	vehicle	NOUN
ajst-31195	102	27	and	and	CCONJ
ajst-31195	102	28	robots	robot	NOUN
ajst-31195	102	29	:	:	PUNCT
ajst-31195	102	30	a	a	DET
ajst-31195	102	31	review	review	NOUN
ajst-31195	103	1	[	[	X
ajst-31195	103	2	j	j	X
ajst-31195	103	3	]	]	X
ajst-31195	103	4	.	.	PUNCT
ajst-31195	104	1	computers	computer	NOUN
ajst-31195	104	2	and	and	CCONJ
ajst-31195	104	3	electronics	electronic	NOUN
ajst-31195	104	4	in	in	ADP
ajst-31195	104	5	agriculture	agriculture	NOUN
ajst-31195	104	6	,	,	PUNCT
ajst-31195	104	7	2023	2023	NUM
ajst-31195	104	8	,	,	PUNCT
ajst-31195	104	9	205	205	NUM
ajst-31195	104	10	:	:	SYM
ajst-31195	104	11	107584	107584	NUM
ajst-31195	104	12	.	.	PUNCT
ajst-31195	105	1	[	[	X
ajst-31195	105	2	2	2	NUM
ajst-31195	105	3	]	]	PUNCT
ajst-31195	105	4	poudel	poudel	NOUN
ajst-31195	105	5	r	r	NOUN
ajst-31195	105	6	p	p	PROPN
ajst-31195	105	7	k	k	PROPN
ajst-31195	105	8	,	,	PUNCT
ajst-31195	105	9	liwicki	liwicki	PROPN
ajst-31195	105	10	s	s	PROPN
ajst-31195	105	11	,	,	PUNCT
ajst-31195	105	12	cipolla	cipolla	PROPN
ajst-31195	105	13	r.	r.	PROPN
ajst-31195	105	14	fast	fast	PROPN
ajst-31195	105	15	-	-	PUNCT
ajst-31195	105	16	scnn	scnn	PROPN
ajst-31195	105	17	:	:	PUNCT
ajst-31195	105	18	fast	fast	ADJ
ajst-31195	105	19	semantic	semantic	ADJ
ajst-31195	105	20	segmentation	segmentation	NOUN
ajst-31195	105	21	network	network	NOUN
ajst-31195	105	22	[	[	X
ajst-31195	105	23	j	j	X
ajst-31195	105	24	]	]	X
ajst-31195	105	25	.	.	PUNCT
ajst-31195	106	1	arxiv	arxiv	PROPN
ajst-31195	106	2	,	,	PUNCT
ajst-31195	106	3	2019	2019	NUM
ajst-31195	106	4	,	,	PUNCT
ajst-31195	106	5	abs/1902.04502.redmon	abs/1902.04502.redmon	PROPN
ajst-31195	106	6	,	,	PUNCT
ajst-31195	106	7	j.	j.	PROPN
ajst-31195	106	8	,	,	PUNCT
ajst-31195	106	9	&	&	CCONJ
ajst-31195	106	10	farhadi	farhadi	PROPN
ajst-31195	106	11	,	,	PUNCT
ajst-31195	106	12	a.	a.	NOUN
ajst-31195	106	13	(	(	PUNCT
ajst-31195	106	14	2018	2018	NUM
ajst-31195	106	15	)	)	PUNCT
ajst-31195	106	16	.	.	PUNCT
ajst-31195	107	1	"	"	PUNCT
ajst-31195	107	2	yolov3	yolov3	PROPN
ajst-31195	107	3	:	:	PUNCT
ajst-31195	107	4	an	an	DET
ajst-31195	107	5	incremental	incremental	ADJ
ajst-31195	107	6	improvement	improvement	NOUN
ajst-31195	107	7	.	.	PUNCT
ajst-31195	107	8	"	"	PUNCT
ajst-31195	108	1	arxiv	arxiv	PROPN
ajst-31195	108	2	preprint	preprint	NOUN
ajst-31195	108	3	arxiv	arxiv	PROPN
ajst-31195	108	4	:	:	PUNCT
ajst-31195	108	5	1804.02767	1804.02767	X
ajst-31195	108	6	.	.	PUNCT
ajst-31195	109	1	[	[	X
ajst-31195	109	2	3	3	X
ajst-31195	109	3	]	]	PUNCT
ajst-31195	109	4	lawal	lawal	NOUN
ajst-31195	109	5	o	o	PROPN
ajst-31195	109	6	m.	m.	NOUN
ajst-31195	109	7	real	real	ADJ
ajst-31195	109	8	-	-	PUNCT
ajst-31195	109	9	time	time	NOUN
ajst-31195	109	10	cucurbit	cucurbit	PROPN
ajst-31195	109	11	fruit	fruit	NOUN
ajst-31195	109	12	detection	detection	NOUN
ajst-31195	109	13	in	in	ADP
ajst-31195	109	14	greenhouse	greenhouse	NOUN
ajst-31195	109	15	using	use	VERB
ajst-31195	109	16	improved	improve	VERB
ajst-31195	109	17	yolo	yolo	ADJ
ajst-31195	109	18	series	series	NOUN
ajst-31195	109	19	algorithm	algorithm	PROPN
ajst-31195	110	1	[	[	X
ajst-31195	110	2	j	j	X
ajst-31195	110	3	]	]	X
ajst-31195	110	4	.	.	PUNCT
ajst-31195	111	1	precision	precision	NOUN
ajst-31195	111	2	agriculture	agriculture	NOUN
ajst-31195	111	3	,	,	PUNCT
ajst-31195	111	4	2024	2024	NUM
ajst-31195	111	5	,	,	PUNCT
ajst-31195	111	6	25(1	25(1	NUM
ajst-31195	111	7	):	):	PUNCT
ajst-31195	111	8	347	347	NUM
ajst-31195	111	9	-	-	SYM
ajst-31195	111	10	59	59	NUM
ajst-31195	111	11	.	.	PUNCT
ajst-31195	112	1	[	[	X
ajst-31195	112	2	4	4	NUM
ajst-31195	112	3	]	]	X
ajst-31195	112	4	guan	guan	PROPN
ajst-31195	112	5	s	s	PROPN
ajst-31195	112	6	,	,	PUNCT
ajst-31195	112	7	liu	liu	PROPN
ajst-31195	112	8	b	b	PROPN
ajst-31195	112	9	,	,	PUNCT
ajst-31195	112	10	chen	chen	PROPN
ajst-31195	112	11	s	s	PROPN
ajst-31195	112	12	,	,	PUNCT
ajst-31195	112	13	et	et	PROPN
ajst-31195	112	14	al	al	PROPN
ajst-31195	112	15	.	.	PROPN
ajst-31195	113	1	adaptive	adaptive	ADJ
ajst-31195	113	2	median	median	ADJ
ajst-31195	113	3	filter	filter	NOUN
ajst-31195	113	4	salt	salt	NOUN
ajst-31195	113	5	and	and	CCONJ
ajst-31195	113	6	pepper	pepper	NOUN
ajst-31195	113	7	noise	noise	NOUN
ajst-31195	113	8	suppression	suppression	NOUN
ajst-31195	113	9	approach	approach	NOUN
ajst-31195	113	10	for	for	ADP
ajst-31195	113	11	common	common	ADJ
ajst-31195	113	12	path	path	NOUN
ajst-31195	113	13	coherent	coherent	ADJ
ajst-31195	113	14	dispersion	dispersion	NOUN
ajst-31195	113	15	spectrometer	spectrometer	NOUN
ajst-31195	114	1	[	[	X
ajst-31195	114	2	j	j	X
ajst-31195	114	3	]	]	X
ajst-31195	114	4	.	.	PUNCT
ajst-31195	115	1	scientific	scientific	ADJ
ajst-31195	115	2	reports	report	NOUN
ajst-31195	115	3	,	,	PUNCT
ajst-31195	115	4	2024	2024	NUM
ajst-31195	115	5	,	,	PUNCT
ajst-31195	115	6	14(1	14(1	NUM
ajst-31195	115	7	):	):	PUNCT
ajst-31195	115	8	17445	17445	NUM
ajst-31195	115	9	.	.	PUNCT
ajst-31195	116	1	[	[	X
ajst-31195	116	2	5	5	NUM
ajst-31195	116	3	]	]	PUNCT
ajst-31195	116	4	jiang	jiang	PROPN
ajst-31195	116	5	l	l	PROPN
ajst-31195	116	6	,	,	PUNCT
ajst-31195	116	7	yuan	yuan	PROPN
ajst-31195	116	8	b	b	PROPN
ajst-31195	116	9	,	,	PUNCT
ajst-31195	116	10	du	du	PROPN
ajst-31195	116	11	j	j	PROPN
ajst-31195	116	12	,	,	PUNCT
ajst-31195	116	13	et	et	PROPN
ajst-31195	116	14	al	al	PROPN
ajst-31195	116	15	.	.	PROPN
ajst-31195	116	16	mffsodnet	mffsodnet	PROPN
ajst-31195	116	17	:	:	PUNCT
ajst-31195	116	18	multiscale	multiscale	ADJ
ajst-31195	116	19	feature	feature	NOUN
ajst-31195	116	20	fusion	fusion	NOUN
ajst-31195	116	21	small	small	ADJ
ajst-31195	116	22	object	object	NOUN
ajst-31195	116	23	detection	detection	NOUN
ajst-31195	116	24	network	network	NOUN
ajst-31195	116	25	for	for	ADP
ajst-31195	116	26	uav	uav	PROPN
ajst-31195	116	27	aerial	aerial	ADJ
ajst-31195	116	28	images	image	NOUN
ajst-31195	116	29	[	[	X
ajst-31195	116	30	j	j	X
ajst-31195	116	31	]	]	X
ajst-31195	116	32	.	.	PUNCT
ajst-31195	117	1	ieee	ieee	NOUN
ajst-31195	117	2	transactions	transaction	NOUN
ajst-31195	117	3	on	on	ADP
ajst-31195	117	4	instrumentation	instrumentation	NOUN
ajst-31195	117	5	and	and	CCONJ
ajst-31195	117	6	measurement	measurement	NOUN
ajst-31195	117	7	,	,	PUNCT
ajst-31195	117	8	2024	2024	NUM
ajst-31195	117	9	,	,	PUNCT
ajst-31195	117	10	73	73	NUM
ajst-31195	117	11	:	:	SYM
ajst-31195	117	12	1	1	NUM
ajst-31195	117	13	-	-	SYM
ajst-31195	117	14	14	14	NUM
ajst-31195	117	15	.	.	PUNCT
ajst-31195	118	1	[	[	X
ajst-31195	118	2	6	6	NUM
ajst-31195	118	3	]	]	PUNCT
ajst-31195	118	4	reddi	reddi	PROPN
ajst-31195	118	5	s	s	PROPN
ajst-31195	118	6	j	j	PROPN
ajst-31195	118	7	,	,	PUNCT
ajst-31195	118	8	kale	kale	PROPN
ajst-31195	118	9	s	s	PROPN
ajst-31195	118	10	,	,	PUNCT
ajst-31195	118	11	kumar	kumar	PROPN
ajst-31195	118	12	s.	s.	PROPN
ajst-31195	118	13	on	on	ADP
ajst-31195	118	14	the	the	DET
ajst-31195	118	15	convergence	convergence	NOUN
ajst-31195	118	16	of	of	ADP
ajst-31195	118	17	adam	adam	PROPN
ajst-31195	118	18	and	and	CCONJ
ajst-31195	118	19	beyond	beyond	ADP
ajst-31195	118	20	[	[	X
ajst-31195	118	21	j	j	X
ajst-31195	118	22	]	]	X
ajst-31195	118	23	.	.	PUNCT
ajst-31195	119	1	corr	corr	PROPN
ajst-31195	119	2	,	,	PUNCT
ajst-31195	119	3	abs/1904.09237	abs/1904.09237	PROPN
ajst-31195	119	4	.	.	PUNCT