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<p>This study investigates the application of YOLOv8 object detection models for identifying and counting oil palm trees in plantation management, with a focus on deployment on edge devices. Various YOLOv8 architectures were trained and evaluated using drone-captured images of palm oil plantations. The YOLOv8 nano model delivered superior performance, achieving 95.6% precision, 93.3% recall, and 98% mAP @0.5. The optimized model was deployed on a Raspberry Pi 4B (RPi) equipped with an Intel Neural Compute Stick 1 (NCS1) accelerator, enabling real-time detection under field conditions. Post-deployment performance analysis indicated an average inference time of 0.4&#xa0;s per image (2.4 FPS) with minimal accuracy reduction (97% mAP @0.5). The system demonstrated low power consumption (1.6W peak) and efficient memory usage (450&#xa0;MB RAM), emphasizing its suitability for edge computing in precision agriculture. This research demonstrates the feasibility of employing compact, efficient deep-learning models on resource-constrained devices like the RPi for palm oil tree detection and counting. The proposed system provides a portable and energy-efficient solution for real-time monitoring of palm oil plantations, offering significant potential to enhance plantation management by enabling data analysis and decision-making in remote or challenging environments. Despite promising results, the system's performance was evaluated under specific environmental conditions, potentially limiting its generalizability across diverse plantation landscapes. Future research should aim to expand detection capabilities to include disease identification and yield estimation.</p> Graphical abstract <p></p>

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Efficient real-time palm oil tree detection and counting using YOLOv8 deployed on edge devices

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月11日

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  • Mohammad Farhan,
  • Mohammad Nishat Akhtar,
  • Elmi Abu Bakar

摘要

This study investigates the application of YOLOv8 object detection models for identifying and counting oil palm trees in plantation management, with a focus on deployment on edge devices. Various YOLOv8 architectures were trained and evaluated using drone-captured images of palm oil plantations. The YOLOv8 nano model delivered superior performance, achieving 95.6% precision, 93.3% recall, and 98% mAP @0.5. The optimized model was deployed on a Raspberry Pi 4B (RPi) equipped with an Intel Neural Compute Stick 1 (NCS1) accelerator, enabling real-time detection under field conditions. Post-deployment performance analysis indicated an average inference time of 0.4 s per image (2.4 FPS) with minimal accuracy reduction (97% mAP @0.5). The system demonstrated low power consumption (1.6W peak) and efficient memory usage (450 MB RAM), emphasizing its suitability for edge computing in precision agriculture. This research demonstrates the feasibility of employing compact, efficient deep-learning models on resource-constrained devices like the RPi for palm oil tree detection and counting. The proposed system provides a portable and energy-efficient solution for real-time monitoring of palm oil plantations, offering significant potential to enhance plantation management by enabling data analysis and decision-making in remote or challenging environments. Despite promising results, the system's performance was evaluated under specific environmental conditions, potentially limiting its generalizability across diverse plantation landscapes. Future research should aim to expand detection capabilities to include disease identification and yield estimation.

Graphical abstract