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Approach to Analysis the Surface Geometry Change in Cultural Heritage Objects Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Deep Transfer Learning Models for Tomato Disease Detection

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

Published in: Image and Signal Processing

Publisher: 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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previous chapter A Bottom-Up Approach for Pig Skeleton Extraction Using RGB Data
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Metadata
Title
Deep Transfer Learning Models for Tomato Disease Detection
Authors
Maryam Ouhami
Youssef Es-Saady
Mohamed El Hajji
Adel Hafiane
Raphael Canals
Mostafa El Yassa
Copyright Year
2020
Book
Image and Signal Processing

Print ISBN: 978-3-030-51934-6

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

Copyright Year: 2020

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

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