An Improved MobileNet for Disease Detection on Tomato Leaves

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 de...

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Bibliographic Details
Published in:Advances in technology innovation 2023-07, Vol.8 (3), p.192-209
Main Authors: Hai Thanh Nguyen, Huong Hoang Luong, Long Bao Huynh, Bao Quoc Hoang Le, Nhan Hieu Doan, Duc Thien Dao Le
Format: Article
Language:English
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Summary: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.
ISSN:2415-0436
2518-2994
DOI:10.46604/aiti.2023.11568