An Improved MobileNet for Disease Detection on Tomato Leaves

Hai Thanh Nguyen; Huong Hoang Luong; Long Bao Huynh; Bao Quoc Hoang Le; Nhan Hieu Doan; Duc Thien Dao Le

doi:10.46604/aiti.2023.11568

Authors

Hai Thanh Nguyen College of Information and Communication Technology, Can Tho University, Can Tho, Vietnam
Huong Hoang Luong Information Technology Department, FPT University, Can Tho, Vietnam
Long Bao Huynh Information Technology Department, FPT University, Can Tho, Vietnam
Bao Quoc Hoang Le Information Technology Department, FPT University, Can Tho, Vietnam
Nhan Hieu Doan Information Technology Department, FPT University, Can Tho, Vietnam
Duc Thien Dao Le Information Technology Department, FPT University, Can Tho, Vietnam

DOI:

https://doi.org/10.46604/aiti.2023.11568

Keywords:

plant diseases, transfer learning, fine-tuning, MobileNet, mobile devices

Abstract

Tomatoes are widely grown vegetables, and farmers face challenges in caring for them, particularly regarding plant diseases. The MobileNet architecture is renowned for its simplicity and compatibility with mobile devices. This study introduces MobileNet as a deep learning model to enhance disease detection efficiency in tomato plants. The model is evaluated on a dataset of 2,064 tomato leaf images, encompassing early blight, leaf spot, yellow curl, and healthy leaves. Results demonstrate promising accuracy, exceeding 0.980 for disease classification and 0.975 for distinguishing between diseases and healthy cases. Moreover, the proposed model outperforms existing approaches in terms of accuracy and training time for plant leaf disease detection.

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