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Design of a Machine Vision-Based Automatic Digging Depth Control System for Garlic Combine Harvester

Author

Listed:
  • Anlan Ding

    (School of Management and Engineering, Nanjing University, Nanjing 210093, China)

  • Baoliang Peng

    (Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China)

  • Ke Yang

    (Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China)

  • Yanhua Zhang

    (Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China)

  • Xiaoxuan Yang

    (School of Management and Engineering, Nanjing University, Nanjing 210093, China)

  • Xiuguo Zou

    (College of Artificial Intelligence, Nanjing Agricultural University, Nanjing 210031, China)

  • Zhangqing Zhu

    (School of Management and Engineering, Nanjing University, Nanjing 210093, China)

Abstract

The digging depth is an important factor affecting the mechanized garlic harvesting quality. At present, the digging depth of the garlic combine harvester (GCH) is adjusted manually, which leads to disadvantages such as slow response, poor accuracy, and being very dependent on the operator’s experience. To solve this problem, this paper proposes a machine vision-based automatic digging depth control system for the original garlic digging device. The system uses the improved YOLOv5 algorithm to calculate the length of the garlic root at the front end of the clamping conveyor chain in real-time, and the calculation result is sent back to the system as feedback. Then, the STM32 microcontroller is used to control the digging depth by expanding and contracting the electric putter of the garlic digging device. The experimental results of the presented control system show that the detection time of the system is 30.4 ms, the average accuracy of detection is 99.1%, and the space occupied by the model deployment is 11.4 MB, which suits the design of the real-time detection of the system. Moreover, the length of the excavated garlic roots is shorter than that of the system before modification, which represents a lower energy consumption of the system and a lower rate of impurities in harvesting, and the modified system is automatically controlled, reducing the operator’s workload.

Suggested Citation

  • Anlan Ding & Baoliang Peng & Ke Yang & Yanhua Zhang & Xiaoxuan Yang & Xiuguo Zou & Zhangqing Zhu, 2022. "Design of a Machine Vision-Based Automatic Digging Depth Control System for Garlic Combine Harvester," Agriculture, MDPI, vol. 12(12), pages 1-19, December.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:12:p:2119-:d:999299
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    References listed on IDEAS

    as
    1. Chuandong Zhang & Huali Ding & Qinfeng Shi & Yunfei Wang, 2022. "Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network," Agriculture, MDPI, vol. 12(8), pages 1-12, August.
    2. Li, Xiang & Ding, Qian & Sun, Jian-Qiao, 2018. "Remaining useful life estimation in prognostics using deep convolution neural networks," Reliability Engineering and System Safety, Elsevier, vol. 172(C), pages 1-11.
    3. Shuofeng Li & Bing Li & Jin Li & Bin Liu & Xin Li, 2022. "Semantic Segmentation Algorithm of Rice Small Target Based on Deep Learning," Agriculture, MDPI, vol. 12(8), pages 1-13, August.
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