Identifikasi Penyakit Daun Kentang Menggunakan Transfer Learning Model EfficientNetB3 Berbasis Mobile
DOI:
https://doi.org/10.35889/jutisi.v15i4.3657Keywords:
potato leaf disease, EfficientNetB3, deep learning, transfer learning, mobile applicationAbstract
Abstract
Potato leaf disease is one of the main factors that can reduce crop quality and yield, thus requiring an accurate and efficient identification method. Manual identification is often inconsistent and time-consuming. This study aims to develop a deep learning-based model using the EfficientNetB3 architecture with a transfer learning approach to identify potato leaf diseases. The dataset was obtained from Kaggle, namely the Potato Disease Leaf Dataset (PLD), and processed through preprocessing stages including augmentation and normalization. The model was trained using the Adam optimizer to achieve optimal performance. The evaluation results indicate that the model achieved a test accuracy of 99.50%, with precision of 1.00, recall of 1.00, and F1-score of 1.00. The model was then deployed into a Flutter-based mobile application and demonstrated the ability to accurately identify real-world data.
Keywords: Potato leaf disease identification; EfficientNetB3; Deep learning; Transfer Learning; TensorFlow Lite.
Abstrak
Penyakit daun kentang merupakan salah satu faktor utama yang dapat menurunkan kualitas dan hasil panen sehingga diperlukan metode identifikasi yang akurat dan efisien. Identifikasi secara manual sering tidak konsisten dan membutuhkan waktu yang relatif lama. Penelitian ini bertujuan untuk mengembangkan model berbasis deep learning menggunakan arsitektur EfficientNetB3 dengan pendekatan transfer learning untuk mengidentifikasi penyakit daun kentang. Dataset yang digunakan berasal dari Kaggle, yaitu Potato Disease Leaf Dataset (PLD), yang diproses melalui tahap preprocessing berupa augmentasi dan normalisasi. Model dilatih menggunakan optimizer Adam guna memperoleh kinerja optimal. Hasil evaluasi menunjukkan bahwa model mencapai test accuracy sebesar 99,50% serta nilai precision sebesar 1,00, recall sebesar 1,00, dan F1-score sebesar 1,00. Model kemudian diimplementasikan ke dalam aplikasi berbasis mobile menggunakan Flutter serta mampu mengidentifikasi data dunia nyata secara akurat.
Kata kunci: Identifikasi penyakit daun kentang; EfficientNetB3; Deep learning; Transfer Learning; TensorFlow Lite.
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