id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-028789-dqa74cus	Ouhami, Maryam	Deep Transfer Learning Models for Tomato Disease Detection	2020-06-05		.txt	text/plain	2695	145	55	The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. In [6] , the study is based on a database of 120 images of infected rice leaves divided into three classes bacterial leaf blight, brown spot, and leaf smut (40 images for each class), Authors have converted the RGB images to an HSV color space to identify lesions, with a segmentation accuracy up to 96.71% using k-means. In plant disease detection field, many researchers have chosen deep models DensNets and VGGs for their high performance in standard computer vision tasks. In this paper we have studied three deep learning models in order to deal with the problem of plant disease detection. From the study that has been conducted it is possible to conclude that DensNet has a suitable architecture for the task of plants disease detection based on crop images. Using deep learning for image-based plant disease detection	./cache/cord-028789-dqa74cus.txt	./txt/cord-028789-dqa74cus.txt