Automated Detection and Classification of Tomato Leaf Diseases Using EfficientNetB0 and Deep Learning Techniques

Muhammad Tariq; Xingjuan Cai; Inam Ullah; Muhammad Afzal Shah; Muhammad Suleman Soomro; Chudary Akbar Ali

Authors

Muhammad Tariq School of Computer Science and Technology (Taiyuan University of Science and Technology, Taiyuan, Shanxi, China).
Xingjuan Cai School of Computer Science and Technology (Taiyuan University of Science and Technology, Taiyuan, Shanxi, China).
Inam Ullah University Institute of Information Technology (PMAS Arid Agriculture University, Rawalpindi, Punjab, Pakistan
Muhammad Afzal Shah School of Electronics Information Engineering (Taiyuan University of Science and Technology, Taiyuan, Shanxi, China)
Muhammad Suleman Soomro School of Electronics Information Engineering (Taiyuan University of Science and Technology, Taiyuan, Shanxi, China)
Chudary Akbar Ali School of Computer Science and Technology (Wuhan Textile University, Wuhan, Hubei, China).

Keywords:

EfficientNetB0, Deep Learning, Tomato Plant Diseases, Image Classification, Transfer Learning

Abstract

Tomato leaf diseases significantly impact agricultural productivity worldwide, necessitating accurate and timely detection methods. This research proposes a robust and efficient deep learning framework leveraging the “EfficientNetB0” architecture for the detection and classification of multiple tomato leaf diseases. Utilizing transfer learning alongside advanced data augmentation techniques, the model was trained on a comprehensive dataset comprising six disease categories and healthy samples, sourced from Kaggle. The proposed approach achieved an overall accuracy of 88.4%, outperforming traditional methods such as CNN, AlexNet, and S-V-M by a notable margin across all disease classes. Evaluation metrics, including precision, recall, and F1-score, further validate the model’s ability to accurately distinguish subtle disease symptoms despite class imbalance challenges. Additionally, the lightweight design of “EfficientNetB0” enables potential real-time applications in mobile and edge computing environments. These findings highlight the model’s promise as an effective tool for precision agriculture, facilitating early disease intervention and reducing crop loss. Future work will focus on expanding the dataset diversity and deploying the system in real-world agricultural settings through mobile and drone platforms.

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