id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-31663	Xue, Yanan; Li, Zhipeng	Research on Non-destructive Detection of Kiwi Fruit Internal Quality Based on Deep Learning	2025	8	.pdf	application/pdf	4450	215	46	To improve the accuracy and efficiency of fruit quality classification, this paper takes kiwifruit as the research object and proposes a non-destructive detection technology that integrates near-infrared spectroscopy technology and deep learning models to achieve kiwifruit quality classification based on soluble solid content (SSC). This study indicates that the combination of near-infrared spectroscopy data with deep learning algorithms, especially BiLSTM and multi-head attention mechanism, can effectively improve the performance of kiwi fruit quality classification, bringing new ideas and methods to the field of fruit quality detection, and meeting the requirements of modern fruit industry for rapid and non-destructive detection.	cache/fcis-31663.pdf	txt/fcis-31663.txt