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2020 | OriginalPaper | Buchkapitel

Deep Transfer Learning Models for Tomato Disease Detection

verfasst von : Maryam Ouhami, Youssef Es-Saady, Mohamed El Hajji, Adel Hafiane, Raphael Canals, Mostafa El Yassa

Erschienen in: Image and Signal Processing

Verlag: Springer International Publishing

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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.

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Titel: Deep Transfer Learning Models for Tomato Disease Detection
verfasst von: Maryam Ouhami
Youssef Es-Saady
Mohamed El Hajji
Adel Hafiane
Raphael Canals
Mostafa El Yassa
Verlag: Springer International Publishing
Buch: Image and Signal Processing
Print ISBN: 978-3-030-51934-6

Electronic ISBN: 978-3-030-51935-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-51935-3_7

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