An Improved MobileNet for Disease Detection on Tomato Leaves


  • 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



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


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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How to Cite

Hai Thanh Nguyen, Huong Hoang Luong, Long Bao Huynh, Bao Quoc Hoang Le, Nhan Hieu Doan, and Duc Thien Dao Le, “An Improved MobileNet for Disease Detection on Tomato Leaves”, Adv. technol. innov., vol. 8, no. 3, pp. 192–209, Jul. 2023.