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Maize Stem Contour Extraction and Diameter Measurement Based on Adaptive Threshold Segmentation in Field Conditions

Author

Listed:
  • Jing Zhou

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Yushan Wu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Jian Chen

    (Changchun Elringklinger Ltd., Changchun 130033, China)

  • Mingren Cui

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Yudi Gao

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Keying Meng

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Min Wu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Xinyu Guo

    (Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

  • Weiliang Wen

    (Beijing Key Laboratory of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

Abstract

Solving the problem of the stem contour extraction of maize is difficult under open field conditions, and the stem diameter cannot be measured quickly and nondestructively. In this paper, maize at the small and large bell stages was the object of study. An adaptive threshold segmentation algorithm based on the color space model was proposed to obtain the stem contour and stem diameter of maize in the field. Firstly, 2D images of the maize stem in the field were captured with an RGB-D camera. Then, the images were processed by hue saturation value (HSV) color space. Next, the stem contour of the maize was extracted by maximum between-class variance (Otsu). Finally, the reference method was used to obtain the stem diameter of the maize. Scatter plots and Dice coefficients were used to compare the contour extraction effects of the HSV + fixed threshold algorithm, the HSV + Otsu algorithm, and the HSV + K-means algorithm. The results showed that the HSV + Otsu algorithm is the optimal choice for extracting the maize stem contour. The mean absolute error, mean absolute percentage error ( MAPE ), and root mean square error ( RMSE ) of the maize stem diameter at the small bell stage were 4.30 mm, 10.76%, and 5.29 mm, respectively. The mean absolute error, MAPE , and RMSE of the stem diameter of the maize at the large bell stage were 4.78 mm, 12.82%, and 5.48 mm, respectively. The MAPE was within 10–20%. The results showed that the HSV + Otsu algorithm could meet the requirements for stem diameter measurement and provide a reference for the acquisition of maize phenotypic parameters in the field. In the meantime, the acquisition of maize phenotypic parameters under open field conditions provides technical and data support for precision farming and plant breeding.

Suggested Citation

  • Jing Zhou & Yushan Wu & Jian Chen & Mingren Cui & Yudi Gao & Keying Meng & Min Wu & Xinyu Guo & Weiliang Wen, 2023. "Maize Stem Contour Extraction and Diameter Measurement Based on Adaptive Threshold Segmentation in Field Conditions," Agriculture, MDPI, vol. 13(3), pages 1-12, March.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:678-:d:1097202
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    References listed on IDEAS

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    1. Yichen Qiao & Yaohua Hu & Zhouzhou Zheng & Zhanghao Qu & Chao Wang & Taifeng Guo & Juncai Hou, 2022. "A Diameter Measurement Method of Red Jujubes Trunk Based on Improved PSPNet," Agriculture, MDPI, vol. 12(8), pages 1-22, August.
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    Cited by:

    1. Nan Lin & Xunhu Ma & Ranzhe Jiang & Menghong Wu & Wenchun Zhang, 2024. "Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm," Agriculture, MDPI, vol. 14(5), pages 1-21, April.

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