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Classification of diseases in tomato leaves with Deep Transfer Learning

By
Noredine Hajraoui ,
Noredine Hajraoui

Moulay Ismail University Faculty of Science and Technology Errachidia, Morocco

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Mourade Azrour ,
Mourade Azrour

Moulay Ismail University Faculty of Science and Technology Errachidia, Morocco

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Ahmad El Allaoui ,
Ahmad El Allaoui

Moulay Ismail University Faculty of Science and Technology Errachidia, Morocco

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Abstract

Plant diseases are important factors because they significantly affect the quality, quantity, and yield of agricultural products. Therefore, it is important to detect and diagnose these diseases at an early stage. The overall objective of this study is to develop an acceptable deep learning model to correctly classify diseases on tomato leaves in RGB color images. To address this challenge, we use a new approach based on combining two deep learning models VGG16 and ResNet152v2 with transfer learning. The image dataset contains 55000 images of tomato leaves in 5 different classes, 4 diseases and one healthy class. The results of our experiment are promising and encouraging, showing that the proposed model achieves 99.08% accuracy in training, 97.66% in validation, and 99.0234% in testing

How to Cite

1.
Hajraoui N, Azrour M, El Allaoui A. Classification of diseases in tomato leaves with Deep Transfer Learning. Data and Metadata [Internet]. 2023 Dec. 1 [cited 2024 Apr. 22];2:181. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/181

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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