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All Journal Jurnal Komputer dan Teknologi (JUKOMTEK)
Cornelis Rasyid, Nanda
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Computer Science & IT Library & Information Science

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KLASIFIKASI JENIS PENYAKIT BUAH MANGGA BERBASIS DEEP LEARNING MENGGUNAKAN ARSITEKTUR RESNET DAN MOBILENET Cornelis Rasyid, Nanda; Karman, Joni; Toyib Hidayat, Asep; Lingga Wijaya, Harma Oktavia
Jurnal Komputer dan Teknologi Vol 5 No 1 (2026): JUKOMTEK JANUARI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i1.570

Abstract

Mango plantations in Indonesia face significant challenges due to pests and diseases that reduce productivity and cause economic losses for farmers. Manual identification of these issues requires expert knowledge and is often time-consuming and inaccurate. This study aims to develop a classification system for detecting various mango leaf diseases using deep learning models, specifically ResNet and MobileNet architectures. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automatic disease detection from plant images by learning patterns without explicit programming. The proposed system focuses on identifying common diseases such as leaf blight, whiteflies, and leaf caterpillars efficiently and accurately. By leveraging image-based recognition, the system allows for early diagnosis and timely intervention. The results of this research are expected to provide a technological solution that supports modern agriculture and empowers farmers with better disease management tools.
Co-Authors Karman, Joni Lingga Wijaya, Harma Oktavia Toyib Hidayat, Asep