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Cocoguard: A Comparative Study of YOLO-Family Models and Faster R-CNN for Coconut Pest Identification With Rule-Based Decision Support

Author

Listed:
  • Maynard A. Ermita

    (BS in Computer Science, CITI Global College Inc., Cabuyao Campus Cabuyao, Laguna)

  • Arlene B. Laurel

    (BS in Computer Science, CITI Global College Inc., Cabuyao Campus Cabuyao, Laguna)

  • Khristian Mae Lee

    (BS in Computer Science, CITI Global College Inc., Cabuyao Campus Cabuyao, Laguna)

  • Erwin C. Martinez

    (BS in Computer Science, CITI Global College Inc., Cabuyao Campus Cabuyao, Laguna)

Abstract

Coconut farming is a vital component of the Philippine agricultural sector; however, destructive pests such as the Asiatic Palm Weevil, Rhinoceros Beetle, Brontispa beetle, and Slug Caterpillar can significantly reduce crop yields when infestations are not detected early. Conventional pest monitoring relies on manual inspection and delayed expert consultation, making pest identification slow and often inaccessible for smallholder farmers. This study proposes CocoGuard, a mobile-accessible artificial intelligence-assisted system for coconut pest detection using deep learning object detection models. A dataset of 2,076 coconut pest images, expanded to 5,398 images through augmentation, was prepared and annotated into seven pest classes. Several object detection models were benchmarked using Precision, Recall, F1-score, and mean Average Precision. Results show that YOLO26s achieved the best performance, obtaining 92.72% mAP@0.5, 92.51% precision, and 89.49% recall while maintaining computational efficiency suitable for mobile deployment.

Suggested Citation

  • Maynard A. Ermita & Arlene B. Laurel & Khristian Mae Lee & Erwin C. Martinez, 2026. "Cocoguard: A Comparative Study of YOLO-Family Models and Faster R-CNN for Coconut Pest Identification With Rule-Based Decision Support," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(4), pages 4790-4802, April.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:4:p:4790-4802
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    References listed on IDEAS

    as
    1. Saim Khalid & Hadi Mohsen Oqaibi & Muhammad Aqib & Yaser Hafeez, 2023. "Small Pests Detection in Field Crops Using Deep Learning Object Detection," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
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