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Detection of Wheat Lodging by Binocular Cameras during Harvesting Operation

Author

Listed:
  • Jingqian Wen

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Yanxin Yin

    (Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China)

  • Yawei Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Zhenglin Pan

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Yindong Fan

    (School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

Abstract

Wheat lodging provides important reference information for self-adaptive header control of a combine harvester. Aimed at real-time detection of wheat lodging, this paper proposed a detection method of wheat lodging location and area based on binocular vision. In this method, the angle relationship between the stem and vertical direction when wheat is upright, inclined, and lodging was determined by mechanical analysis. The discrimination condition of the wheat lodging degree was proposed based on the height of the visual point cloud on the surface of wheat crops. The binocular camera was used to obtain the image parallax of wheat within the harvesting region. The binocular camera optical axis parallel model was used to calculate the three-dimensional coordinate of wheat. Then, the height of the wheat stem was obtained by further analysis and calculation. According to the wheat stem height detected by vision, the location and area of wheat lodging within the combine harvester’s harvesting region were analyzed. A field experiment showed that the detection error of the wheat stem height was 5.5 cm and the algorithm speed was under 2000 milliseconds, which enabled the analysis and calculation of the wheat lodging location, contour, and area within the combine harvester’s harvesting region. This study provides key information for adaptive header control of combine harvesters.

Suggested Citation

  • Jingqian Wen & Yanxin Yin & Yawei Zhang & Zhenglin Pan & Yindong Fan, 2022. "Detection of Wheat Lodging by Binocular Cameras during Harvesting Operation," Agriculture, MDPI, vol. 13(1), pages 1-14, December.
    • Handle: RePEc:gam:jagris:v:13:y:2022:i:1:p:120-:d:1021861
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    References listed on IDEAS

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    1. Shan Hua & Minjie Xu & Zhifu Xu & Hongbao Ye & Cheng quan Zhou, 2021. "Kinect-Based Real-Time Acquisition Algorithm of Crop Growth Depth Images," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, July.
    2. Longfei Zhou & Xiaohe Gu & Shu Cheng & Guijun Yang & Meiyan Shu & Qian Sun, 2020. "Analysis of Plant Height Changes of Lodged Maize Using UAV-LiDAR Data," Agriculture, MDPI, vol. 10(5), pages 1-14, May.
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    Cited by:

    1. Xiaobo Zhuang & Yaoming Li, 2023. "Segmentation and Angle Calculation of Rice Lodging during Harvesting by a Combine Harvester," Agriculture, MDPI, vol. 13(7), pages 1-15, July.

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