Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks

Saeed, Alaa; Abdel-Aziz, A. A.; Mossad, Amr; Abdelhamid, Mahmoud A.; Alkhaled, Alfadhl Y.; Mayhoub, Muhammad

doi:10.3390/agriculture13010139

Open AccessArticle

Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks

by

Alaa Saeed

^1,*,

A. A. Abdel-Aziz

¹,

Amr Mossad

¹

,

Mahmoud A. Abdelhamid

¹

,

Alfadhl Y. Alkhaled

² and

Muhammad Mayhoub

¹

Agricultural Engineering Department, Ain Shams University, Cairo 11221, Egypt

²

Department of Horticulture, College of Agricultural & Life Sciences, University of Wisconsin-Madison, Madison, WI 53705, USA

^*

Author to whom correspondence should be addressed.

Agriculture 2023, 13(1), 139; https://doi.org/10.3390/agriculture13010139

Submission received: 8 December 2022 / Revised: 26 December 2022 / Accepted: 3 January 2023 / Published: 5 January 2023

(This article belongs to the Section Agricultural Technology)

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Abstract

Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection methods that can be used to reduce plant losses due to disease. Thus, this study aims to diagnose tomato leaf diseases by classifying healthy and unhealthy tomato leaf images using two pre-trained convolutional neural networks (CNNs): Inception V3 and Inception ResNet V2. The two models were trained using an open-source database (PlantVillage) and field-recorded images with a total of 5225 images. The models were investigated with dropout rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50%. The most important results showed that the Inception V3 model with a 50% dropout rate and the Inception ResNet V2 model with a 15% dropout rate, as they gave the best performance with an accuracy of 99.22% and a loss of 0.03. The high-performance rate shows the possibility of utilizing CNNs models for diagnosing tomato diseases under field and laboratory conditions. It is also an approach that can be expanded to support an integrated system for diagnosing various plant diseases.

Keywords: deep learning; convolutional neural networks; inception V3; inception ResNet V2; tomato disease diagnosis

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MDPI and ACS Style

Saeed, A.; Abdel-Aziz, A.A.; Mossad, A.; Abdelhamid, M.A.; Alkhaled, A.Y.; Mayhoub, M. Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture 2023, 13, 139. https://doi.org/10.3390/agriculture13010139

AMA Style

Saeed A, Abdel-Aziz AA, Mossad A, Abdelhamid MA, Alkhaled AY, Mayhoub M. Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture. 2023; 13(1):139. https://doi.org/10.3390/agriculture13010139

Chicago/Turabian Style

Saeed, Alaa, A. A. Abdel-Aziz, Amr Mossad, Mahmoud A. Abdelhamid, Alfadhl Y. Alkhaled, and Muhammad Mayhoub. 2023. "Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks" Agriculture 13, no. 1: 139. https://doi.org/10.3390/agriculture13010139

APA Style

Saeed, A., Abdel-Aziz, A. A., Mossad, A., Abdelhamid, M. A., Alkhaled, A. Y., & Mayhoub, M. (2023). Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks. Agriculture, 13(1), 139. https://doi.org/10.3390/agriculture13010139

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Smart Detection of Tomato Leaf Diseases Using Transfer Learning-Based Convolutional Neural Networks

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