Enhancing Agricultural Pest Management with YOLO V5: A Detection and Classification Approach

  • Asif Raza Department of Software Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan
  • Muhammad Kashif Shaikh Department of Software Engineering, Sir Syed University of Engineering and Technology,Karachi, Pakistan
  • Osama Ahmed Siddiqui Department of Computer Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan
  • Asher Ali Department of Computer Science and Information Technology, Karachi, Pakistan
  • Afshan Khan Department of Computer Science and Information Technology, Karachi, Pakistan
Keywords: learning, yolo, architecture, accuracy, crop, ecological, biodiversity


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Due to the growing population and the numerous ecological challenges that affect crop yields, the need to modernize the crop production process has become increasingly critical. Swiftly managing potential threats to crops can have a substantial impact on overall crop production. Pests represent a significant menace, capable of causing substantial losses if not effectively controlled in a timely manner. In this study, a deep learning-based method for pest identification is introduced. The approach leverages the YOLO (You Only Look Once) object recognition SSD (single shot detection )algorithm in combination with the pre-trained DARKNET architecture to categorize pests into nine distinct classes. The study utilizes a publicly available dataset sourced from Kaggle, which comprises a total of 7,046 images. The outcomes reveal overall 83% of overall accuracy rate, with a notably low training and validation loss of 0.02%. Moreover, our model exhibits a notable enhancement in localization results, delivering a precision of 0.83, a recall of 0.83, an mAP-0.5 of 0.833, and an mAP-0.5:0.95 of 0.783.


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How to Cite
Raza, A., Shaikh, M. K., Ahmed Siddiqui, O., Ali, A., & Khan, A. (2023). Enhancing Agricultural Pest Management with YOLO V5: A Detection and Classification Approach. UMT Artificial Intelligence Review, 3(2). https://doi.org/10.32350/umt-air.32.02