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All Journal International Journal of Informatics and Computation
Ego Oktafanda
Universitas Rokania, Indonesia
Computer Science & IT

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Detection of Oil Palm Seedling Disease Based on Leaf Images Using the MobileNetV2-CNN Architecture Ego Oktafanda; Adyanata Lubis; Elyandri Prasiwiningrum
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.71

Abstract

This study aims to develop and implement a plant disease detection system for oil palm seedlings based on leaf images using the MobileNetV2 architecture, which is based on Convolutional Neural Networks (CNN). The model was trained using a dataset of oil palm leaf images to detect several types of plant diseases. In the experiments, the applied model showed excellent results, with training accuracy increasing from 79% in the first epoch to 96% in the 15 epoch, and validation accuracy also increasing from 89% to 97%. These results demonstrate that the model can effectively detect plant diseases with good generalization ability on unseen data. With stable loss reduction and continuously improving accuracy, this study proves that the MobileNetV2 architecture can be efficiently used for plant disease detection. The research also highlights the potential integration of the model into an application to provide a practical solution in oil palm plantation management and to support decision-making and improve agricultural outcomes.
Co-Authors Adyanata Lubis Elyandri Prasiwiningrum