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  3. Ai-enabled Crop Management Framework For Pest Detection Using Visual Sensor Data.
  1. Home
  3. Ai-enabled Crop Management Framework For Pest Detection Using Visual Sensor Data.

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AI-Enabled Crop Management Framework for Pest Detection Using Visual Sensor Data.

Asma Khan1, Sharaf J Malebary2, L Minh Dang3

  • 1Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea.

Plants (Basel, Switzerland)
|March 13, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an optimized YOLOv5s model for drone-based pest detection in agriculture. The enhanced model achieves high precision and recall, revolutionizing crop monitoring and pest management.

Keywords:
UAV technologycomputer visionconvolution neural networkdeep learningmonitoring systemsustainable agriculture

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Robotics

Background:

  • Crop diseases and pest infestations pose significant threats to agricultural productivity.
  • Traditional pest detection methods are often labor-intensive or computationally expensive.
  • Unmanned Aerial Vehicle (UAV) technology offers potential for efficient agricultural monitoring.

Purpose of the Study:

  • To develop and evaluate an optimized deep learning model for UAV-based agricultural pest detection and classification.
  • To improve the accuracy and efficiency of pest identification compared to existing methods.
  • To contribute to sustainable agriculture through advanced pest management strategies.

Main Methods:

  • Modification of the YOLOv5s model with advanced attention modules, expanded CSP modules, and refined feature extraction.
  • Utilizing UAVs for aerial data acquisition in agricultural settings.
  • Training and testing the model on a dataset comprising five common agricultural pests.

    • Main Results:

    • The proposed optimized YOLOv5s model demonstrated superior performance in pest detection and classification.
    • Achieved an average precision of 96.0%, average recall of 93.0%, and mean average precision (mAP) of 95.0%.
    • Outperformed various standard YOLOv5 model versions in experimental tests.

    Conclusions:

    • The optimized YOLOv5s model provides a precise and efficient solution for real-time pest detection using UAVs.
    • This technology has significant potential to enhance agricultural production and disease prevention within drone-centric ecosystems.
    • The study highlights the effectiveness of integrating advanced deep learning with UAVs for sustainable pest management.