id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cana-4558	A.Yoganathan	Accurate Identification of Leaf Disease using Yolov4 Algorithm	2025	10	.pdf	application/pdf	3857	275	44	0.94 0.87 0.93 0.86 0.93 0.87 13. 0.94 0.88 0.94 0.87 0.94 0.88 14. 0.95 0.89 0.94 0.88 0.94 0.89 Table 2 represents the leaf disease detection accuracy of the comparison between YOLOv4 model and CNN models for precision agriculture was YOLOv4 mean accuracy with a standard deviation and a Communications on Applied Nonlinear Analysis ISSN: 1074-133X Vol 32 No. 9s (2025) 2535 https://internationalpubls.com standard error mean of [X]. Table 1 shows the comparison of accuracy, precision, and recall values of YOLOv4 and CNN models in leaf disease detection with various agricultural field conditions, involving challenging conditions of fluctuating lighting, overlapping leaves, and heterogeneous layouts.	cache/cana-4558.pdf	txt/cana-4558.txt