About: Deep Transfer Learning Models for Tomato Disease Detection

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Deep Transfer Learning Models for Tomato Disease Detection
Creator	Canals, Raphael El, Mohamed Es-Saady, Youssef Hafiane, Adel Ouhami, Maryam Yassa, Mostafa
source	PMC
abstract	Vegetable crops in Morocco and especially in the Sous-Massa region are exposed to parasitic diseases and pest attacks which affect the quantity and the quality of agricultural production. Precision farming is introduced as one of the biggest revolutions in agriculture, which is committed to improving crop protection by identifying, analyzing and managing variability delivering effective treatment in the right place, at the right time, and with the right rate. The main purpose of this study is to find the most suitable machine learning model to detect tomato crop diseases in standard RGB images. To deal with this problem we consider the deep learning models DensNet, 161 and 121 layers and VGG16 with transfer learning. Our study is based on images of infected plant leaves divided into 6 types of infections pest attacks and plant diseases. The results were promising with an accuracy up to 95.65% for DensNet161, 94.93% for DensNet121 and 90.58% for VGG16.
has issue date	2020-06-05(xsd:dateTime)
bibo:doi	10.1007/978-3-030-51935-3_7
has license	no-cc
sha1sum (hex)	b8cfbc96a86a8f3e10d48fa9938093b3bc11e79f
schema:url	https://doi.org/10.1007/978-3-030-51935-3_7
resource representing a document's title	Deep Transfer Learning Models for Tomato Disease Detection
has PubMed Central identifier	PMC7340929
schema:publication	Image and Signal Processing
resource representing a document's body	covid:b8cfbc96a86a8f3e10d48fa9938093b3bc11e79f#body_text
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