Advanced Image Recognition Techniques for Crop Pest Detection Using Modified YOLO-v3

Accurate and efficient detection of agricultural pests is crucial for crop protection and pest control. This study addresses the limitations of traditional pest detection methods, such as weak detection capabilities and high computational demands, by proposing an improved image recognition system based on the YOLO-v3 algorithm. The research focuses on enhancing pest detection accuracy through deep learning techniques, specifically by modifying the YOLO-v3 model with the ISODATA clustering algorithm, DenseBlock enhancements, and the ELU activation function. A dataset of 13,000 images representing six common crop pests was created and expanded using various image augmentation techniques. The modified YOLO-v3 model was trained and evaluated on this dataset, achieving a higher mean Average Precision (mAP) of 89.7% and faster recognition speed compared to Faster-RCNN, SSD-300, and the original YOLO-v3 model. Finally, the improved model demonstrated a recognition speed of 27 frames per second (fps), significantly outperforming other detection models in both accuracy and speed. The proposed method offers a superior solution for real-time pest detection in agricultural settings, combining high accuracy with computational efficiency. Future work will explore the application of optimization algorithms to further enhance the robustness and generalizability of the system across diverse pest detection scenarios.

@article{Guo2025,
  title     = {Advanced Image Recognition Techniques for Crop Pest Detection Using Modified YOLO-v3},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Dechao Guo and Hao Zhang},
  doi       = {10.14569/IJACSA.2025.0160587},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160587}
}