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Tea Tree Pest Detection Algorithm Based on Improved Yolov7-Tiny

Author

Listed:
  • Zijia Yang

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
    Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China
    China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China)

  • Hailin Feng

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
    Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China
    China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China)

  • Yaoping Ruan

    (College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
    Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China
    China Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China)

  • Xiang Weng

    (College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Lin′an, Hangzhou 311300, China)

Abstract

Timely and accurate identification of tea tree pests is critical for effective tea tree pest control. We collected image data sets of eight common tea tree pests to accurately represent the true appearance of various aspects of tea tree pests. The dataset contains 782 images, each containing 1~5 different pest species randomly distributed. Based on this dataset, a tea garden pest detection and recognition model was designed using the Yolov7-tiny network target detection algorithm, which incorporates deformable convolution, the Biformer dynamic attention mechanism, a non-maximal suppression algorithm module, and a new implicit decoupling head. Ablation experiments were conducted to compare the performance of the models, and the new model achieved an average accuracy of 93.23%. To ensure the validity of the model, it was compared to seven common detection models, including Efficientdet, Faster Rcnn, Retinanet, DetNet, Yolov5s, YoloR, and Yolov6. Additionally, feature visualization of the images was performed. The results demonstrated that the Improved Yolov7-tiny model developed was able to better capture the characteristics of tea tree pests. The pest detection model proposed has promising application prospects and has the potential to reduce the time and economic cost of pest control in tea plantations.

Suggested Citation

  • Zijia Yang & Hailin Feng & Yaoping Ruan & Xiang Weng, 2023. "Tea Tree Pest Detection Algorithm Based on Improved Yolov7-Tiny," Agriculture, MDPI, vol. 13(5), pages 1-22, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:5:p:1031-:d:1142915
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    Citations

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    Cited by:

    1. Yuzhe Bai & Fengjun Hou & Xinyuan Fan & Weifan Lin & Jinghan Lu & Junyu Zhou & Dongchen Fan & Lin Li, 2023. "A Lightweight Pest Detection Model for Drones Based on Transformer and Super-Resolution Sampling Techniques," Agriculture, MDPI, vol. 13(9), pages 1-23, September.
    2. Wenji Yang & Xiaoying Qiu, 2024. "A Novel Crop Pest Detection Model Based on YOLOv5," Agriculture, MDPI, vol. 14(2), pages 1-23, February.
    3. Juanli Jing & Menglin Zhai & Shiqing Dou & Lin Wang & Binghai Lou & Jichi Yan & Shixin Yuan, 2024. "Optimizing the YOLOv7-Tiny Model with Multiple Strategies for Citrus Fruit Yield Estimation in Complex Scenarios," Agriculture, MDPI, vol. 14(2), pages 1-16, February.
    4. Yaxin Wang & Xinyuan Liu & Fanzhen Wang & Dongyue Ren & Yang Li & Zhimin Mu & Shide Li & Yongcheng Jiang, 2023. "Self-Attention-Mechanism-Improved YoloX-S for Briquette Biofuels Object Detection," Sustainability, MDPI, vol. 15(19), pages 1-16, October.

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