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All Journal Jurnal Teknik Informatika dan Teknologi Informasi
Hardy Gustino
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Computer Science & IT

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Deteksi Penyakit Daun Terong Menggunakan MobileNetV2 Hardy Gustino; Muhammad Rafi Winno Pratama; Rafli Aldrian Kurnianto; Anggraini Puspita Sari
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5433

Abstract

Eggplant is a horticultural crop that is highly dependent on the health of its leaves to support growth and productivity. Leaf diseases can cause a significant reduction in crop yield if not detected early. This study aims to develop a leaf classification model for eggplant using the MobileNetV2 architecture to automatically detect leaf conditions. The model was trained using a public dataset of eggplant leaf images, with an 80% training and 20% validation data split. During the twenty-epoch training process, the model achieved a validation accuracy of 93%. The final model is stored in a lightweight format. The results of this study indicate that this approach is effective for detecting diseases in eggplant leaves and has the potential to support the implementation of responsive smart agriculture in the field.
Co-Authors Anggraini Puspita Sari Muhammad Rafi Winno Pratama Rafli Aldrian Kurnianto