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Sunardi
Program Studi Teknik Elektro Universitas Ahmad Dahlan
Computer Science & IT

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Model Prediktif Penyakit Tanaman Tomat Menggunakan Arsitektur EfficientNetB0 Tri Ferga Prasetyo; Sunardi; Abdul Fadlil
TEMATIK Vol. 12 No. 2 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i2.2722

Abstract

Tomato (Solanum lycopersicum) is highly vulnerable to a range of foliar diseases that can reduce yield and hinder crop quality, particularly when early symptoms are difficult to distinguish in field conditions. To address this challenge, this study develops a predictive model for automatic tomato leaf disease classification using a lightweight EfficientNetB0 architecture. The dataset consists of 5,967 images from nine categories, combining 70% publicly available Kaggle PlantVillage data and 30% real-field images captured under natural outdoor illumination. The methodological pipeline includes preprocessing, data augmentation, and transfer learning, followed by fine-tuning of the upper layers of EfficientNetB0 to improve its ability to generalize toward field-specific variations such as uneven lighting and complex backgrounds. Evaluation results show that the model achieves an accuracy of 89%, with macro-average and weighted-average scores of 90%. These findings demonstrate that EfficientNetB0 provides an effective balance between predictive accuracy and computational efficiency, supporting its potential deployment in early detection systems and edge-based agricultural applications for real-time tomato disease monitoring.
Co-Authors Abdul Fadlil Tri Ferga Prasetyo