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All Journal Best : Journal of Applied Electrical, Science and Technology
Yunnerja
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

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Exploration of RTMDet and SAHI for Long-Tailed Macaque Damage in Corn Fields Yunnerja; Finki Dona Marleny; Ayu Ahadi Ningrum
BEST Vol 8 No 2 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/ndc0dc33

Abstract

Attacks by long-tailed macaques (Macaca fascicularis) on corn farmland cause a drastic decline in yield from 7–9 metric tons to only 4–5 metric tons per hectare. Traditional control methods have proven ineffective, necessitating solutions based on smart technology. This study explores the RTMDet (Real-Time Detection Transformer) algorithm combined with the SAHI (Slicing Aided Hyper Inference) framework to evaluate wildlife detection performance. Four RTMDet variants RTMDet-tiny, RTMDet-s, RTMDet-m, and RTMDet-l were tested using a dataset of 2,271 COCO annotated images. Evaluation on the test set showed that the standalone RTMDet-s achieved the best performance with a mAP@0.5 of 0.9070, a Recall of 0.9585, and an F1-Score of 0.9321. Applying SAHI reduced accuracy and caused computational delays (down to 3.02 FPS) because the monkey objects occupy a relatively large area within the image frame. Based on these results, the standard RTMDet-s is highly recommended for integration as a real-time automated trigger system within IoT-based wildlife deterrent devices to instantly mitigate attacks and protect farmers’ crop yields.
Co-Authors Ayu Ahadi Ningrum Finki Dona Marleny