Android Application for Tomato Leaf Disease Prediction Based on MobileNet Fine-tuning

Fadhilla, Mutia and Suryani, Des (2023) Android Application for Tomato Leaf Disease Prediction Based on MobileNet Fine-tuning. Accredited Ranking SINTA 2, 7 (6). pp. 1260-1267. ISSN 2580-0760

Text Resti 2023 (Des Suryani).pdf - Published Version Download (722kB)

Abstract

Tomato is one of the most well-known and widely cultivated plants in the world. Tomato production result is affected by the conditions of the plants when they are cultivated. It may decrease due to leaf plant disease caused by climate change, pollinator decrease, microbial pets, or parasites. To prevent this, an image-based application is needed to identify tomato plant disease based on visually unique patterns or marks seen on leaves. In this paper, we proposed a CNN fine-tuned model that is based on MobileNet architectures to identify tomato leaf disease for mobile applications. Based on the results tested by K-fold cross�validation, the best accuracy achieved by the proposed model is 97.1%. In addition, the best average precision, recall, and F1 Score are 99.8%, 99.8%, and 99.5% respectively. The model with have best results is also implemented into Android-based mobile applications.

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