Advancing Food Security through Precision Agriculture: YOLOv8’s Role in Efficient Pest Detection and Management

Ameer Tamoor Khan; Sign Marie Jensen; Noman Khan

doi:10.4108/airo.8049

Authors

Ameer Tamoor Khan University of Copenhagen https://orcid.org/0000-0001-6838-992X
Sign Marie Jensen University of Copenhagen
Noman Khan Pakistan Institute of Engineering and Applied Sciences

DOI:

https://doi.org/10.4108/airo.8049

Keywords:

Sustainable Agriculture, Pest Detection, YOLOv8, Computer Vision, Object Detection, Crop Management, Deep Learning

Abstract

In response to the growing global population and the consequent need for sustainable food security, effective pest management is critical for enhancing agricultural productivity. This research presents YOLOv8, a state-of-the-art deep learning model optimized for pest detection in agricultural environments, contributing to modern food security efforts. Evaluated using the complex IP102 dataset, YOLOv8 demonstrated notable improvements in pest detection accuracy, achieving scores of 66.9 mAP@0.5 and 42.1 mAP@[0.5:0.95]. These results underscore YOLOv8’s robust performance across diverse detection scenarios, enabling more precise pest control and reducing crop loss. However, in-depth dataset analysis revealed a bias towards larger pests, likely due to bounding box size variations, which presents an opportunity for model improvement. Future work will focus on addressing data imbalances, enhancing sensitivity to smaller pests, and validating YOLOv8 in varied real-world agricultural settings. These advancements are expected to significantly improve pest management practices, ultimately boosting agricultural productivity and supporting global food security through the application of modern agricultural technologies.

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