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

Alaa Saeed (), A. A. Abdel-Aziz, Amr Mossad, Mahmoud A. Abdelhamid, Alfadhl Y. Alkhaled and Muhammad Mayhoub
Additional contact information
Alaa Saeed: Agricultural Engineering Department, Ain Shams University, Cairo 11221, Egypt
A. A. Abdel-Aziz: Agricultural Engineering Department, Ain Shams University, Cairo 11221, Egypt
Amr Mossad: Agricultural Engineering Department, Ain Shams University, Cairo 11221, Egypt
Mahmoud A. Abdelhamid: Agricultural Engineering Department, Ain Shams University, Cairo 11221, Egypt
Alfadhl Y. Alkhaled: Department of Horticulture, College of Agricultural & Life Sciences, University of Wisconsin-Madison, Madison, WI 53705, USA
Muhammad Mayhoub: Agricultural Engineering Department, Ain Shams University, Cairo 11221, Egypt

Agriculture, 2023, vol. 13, issue 1, 1-14

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/1/139/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/1/139/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:1:p:139-:d:1025978

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().