id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
cana-5263	Khilendra Tumareki	Deep Learning-Based Crop Disease Detection using IoT and Image Processing	2025	10	.pdf	application/pdf	3395	163	34	The model achieved an impressive accuracy of 99.34%, which indicates the robustness and reliability of the deep learning-based approach for crop disease classification. This paper outlines the methodology for crop disease detection, explains the deep learning models and techniques employed, and discusses the experimental results obtained from the proposed system.	cache/cana-5263.pdf	txt/cana-5263.txt