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All Journal Jurnal Computer Science and Information Technology (CoSciTech) Journal of Education Religion Humanities and Multidiciplinary
Arianda, Habil Putra
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Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions Nursing Social Sciences

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Implementasi Deep Learning Untuk Klasifikasi Penyakit Pada Daun Kelapa Sawit Menggunakan Arsitektur MobileNetV2 Arianda, Habil Putra; Hadiwandra, T. Yudi
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.10306

Abstract

Accurate and efficient identification of diseases in oil palm leaves is a crucial challenge in maintaining plantation productivity and preventing significant crop losses. Limited access to experts and slow detection in the field are often obstacles. This study aims to develop a palm oil leaf disease classification model using a deep learning approach based on Convolutional Neural Network (CNN) with MobileNetV2 architecture. This model utilizes a transfer learning strategy from pre-trained ImageNet weights and is optimized through a two-phase training strategy on a dataset consisting of 1200 augmented oil palm leaf images, covering four classes, namely Healthy Sample, Fusarium Wilt, Parlatoria Blanchardi, and Rachis Blight. Model testing results show an accuracy of 85% on separate test data. The MobileNetV2 architecture was chosen for its lightweight characteristics, making this model efficient and highly suitable for implementation on mobile devices to assist in rapid disease identification in the field and support decision-making by farmers.
Co-Authors Hadiwandra, T Yudi Linna Oktaviana Sari Syah, Maulana Syahrul