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All Journal International Journal of Advances in Data and Information Systems
Yeza, Ardhan
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Computer Science & IT Electrical & Electronics Engineering

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Automated Oil Palm Health Assessment Using Object-Based Deep Learning and High-Resolution UAV Imagery in Indonesia Pindarwati, Atut; Wijayanto, Arie Wahyu; Karmawan, I Putu Agus; Yeza, Ardhan; Sakka, Asriadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1391

Abstract

Indonesia, as the world’s largest crude palm oil (CPO) producer, faces challenges in plantation monitoring due to reliance on manual data collection methods that are time-consuming, costly, and prone to human error. This study proposes an automated approach for assessing oil palm tree health using high-resolution UAV imagery (5–10 cm) and object-based deep learning models. We evaluate five state-of-the-art detectors—YOLOv5s, Faster R-CNN, Mask R-CNN, SSD, and RetinaNet—to classify individual trees into four health categories: Healthy, Moderately Healthy, Needs Improvement, and Urgent Condition. Using a dataset of 14,749 labeled trees from Kendawangan, Indonesia, YOLOv5s achieved the highest performance with a precision of 0.784, recall of 0.752, and mAP of 0.764. Our findings demonstrate the potential of AI-driven monitoring to enhance plantation management through rapid, accurate, and cost-effective health assessments—contributing a scalable solution to support precision agriculture and sustainable CPO production.
Co-Authors Karmawan, I Putu Agus Pindarwati, Atut Sakka, Asriadi