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Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation

Author

Listed:
  • Ranbing Yang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
    College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Yuming Zhai

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Jian Zhang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
    College of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China)

  • Huan Zhang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Guangbo Tian

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Jian Zhang

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

  • Peichen Huang

    (College of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China)

  • Lin Li

    (College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China)

Abstract

Potato machinery has become more intelligent thanks to advancements in autonomous navigation technology. The effect of crop row segmentation directly affects the subsequent extraction work, which is an important part of navigation line detection. However, the shape differences of crops in different growth periods often lead to poor image segmentation. In addition, noise such as field weeds and light also affect it, and these problems are difficult to address using traditional threshold segmentation methods. To this end, this paper proposes an end-to-end potato crop row detection method. The first step is to replace the original U-Net’s backbone feature extraction structure with VGG16 to segment the potato crop rows. Secondly, a fitting method of feature midpoint adaptation is proposed, which can realize the adaptive adjustment of the vision navigation line position according to the growth shape of a potato. The results show that the method used in this paper has strong robustness and can accurately detect navigation lines in different potato growth periods. Furthermore, compared with the original U-Net model, the crop row segmentation accuracy is improved by 3%, and the average deviation of the fitted navigation lines is 2.16°, which is superior to the traditional visual guidance method.

Suggested Citation

  • Ranbing Yang & Yuming Zhai & Jian Zhang & Huan Zhang & Guangbo Tian & Jian Zhang & Peichen Huang & Lin Li, 2022. "Potato Visual Navigation Line Detection Based on Deep Learning and Feature Midpoint Adaptation," Agriculture, MDPI, vol. 12(9), pages 1-17, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1363-:d:904304
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    References listed on IDEAS

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    1. Prakhar Bansal & Rahul Kumar & Somesh Kumar, 2021. "Disease Detection in Apple Leaves Using Deep Convolutional Neural Network," Agriculture, MDPI, vol. 11(7), pages 1-23, June.
    2. Rong Xiang & Maochen Zhang & Jielan Zhang, 2022. "Recognition for Stems of Tomato Plants at Night Based on a Hybrid Joint Neural Network," Agriculture, MDPI, vol. 12(6), pages 1-21, May.
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    Cited by:

    1. Jinkang Jiao & Ying Zang & Chaowen Chen, 2024. "Key Technologies of Intelligent Weeding for Vegetables: A Review," Agriculture, MDPI, vol. 14(8), pages 1-41, August.
    2. Zhongyang Ma & Gang Wang & Jurong Yao & Dongyan Huang & Hewen Tan & Honglei Jia & Zhaobo Zou, 2023. "An Improved U-Net Model Based on Multi-Scale Input and Attention Mechanism: Application for Recognition of Chinese Cabbage and Weed," Sustainability, MDPI, vol. 15(7), pages 1-17, March.
    3. Jiayou Shi & Yuhao Bai & Jun Zhou & Baohua Zhang, 2023. "Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments," Agriculture, MDPI, vol. 14(1), pages 1-22, December.

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