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Research and Experiment on Miss-Seeding Detection of Potato Planter Based on Improved YOLOv5s

Author

Listed:
  • Hongling Li

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Xiaolong Liu

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Hua Zhang

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Hui Li

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Shangyun Jia

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Wei Sun

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Guanping Wang

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Quan Feng

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Sen Yang

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

  • Wei Xing

    (College of Mechanical and Electrical Engineering, Gansu Agricultural University, Lanzhou 730070, China)

Abstract

In order to improve the performance of potato planter, reduce miss-seeding rates, enhance the overall quality of the seeding operation, and ultimately increase the yield of the potato, it is necessary to implement effective technical means to monitor and identify the miss-seeding issues during the seeding process. The existing miss-seeding detection technologies commonly use sensors to monitor, but such technologies are easily affected by factors like heavy dust and strong vibrations, resulting in poor interference resistance and adaptability. Therefore, this study aims to explore and apply deep learning algorithms to achieve real-time monitoring of the miss-seeding phenomenon in potato planter during the planting process. Considering both the lightweight of the miss-seeding detection model and its practical deployment, this study selects and adapts the YOLOv5s algorithm to achieve this goal. Firstly, the attention mechanism is integrated into the backbone network to suppress background interference and improve detection accuracy. Secondly, the non-maximum suppression algorithm is improved by replacing the original IoU-NMS with the Soft-NMS algorithm to enhance the bounding box regression rate and reduce missed detections of potato seeds due to background overlap or occlusion. Experimental results show that the accuracy of the improved algorithm in detecting miss-seeding increased from 96.02% to 98.30%, the recall rate increased from 96.31% to 99.40%, and the mean average precision ( mAP ) improved from 99.12% to 99.40%. The improved model reduces missed and false detections, provides more precise target localization, and is suitable for miss-seeding detection in natural environments for potato planter, providing technical and theoretical support for subsequent intelligent reseeding in potato planter.

Suggested Citation

  • Hongling Li & Xiaolong Liu & Hua Zhang & Hui Li & Shangyun Jia & Wei Sun & Guanping Wang & Quan Feng & Sen Yang & Wei Xing, 2024. "Research and Experiment on Miss-Seeding Detection of Potato Planter Based on Improved YOLOv5s," Agriculture, MDPI, vol. 14(11), pages 1-18, October.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:1905-:d:1507554
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    References listed on IDEAS

    as
    1. Baidong Zhou & Yexin Li & Cong Zhang & Liewang Cao & Chengsong Li & Shouyong Xie & Qi Niu, 2022. "Potato Planter and Planting Technology: A Review of Recent Developments," Agriculture, MDPI, vol. 12(10), pages 1-28, October.
    2. Xinle Zhang & Jian Cui & Huanjun Liu & Yongqi Han & Hongfu Ai & Chang Dong & Jiaru Zhang & Yunxiang Chu, 2023. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    3. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
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