Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (2): 424-431.DOI: 10.3969/j.issn.1004-1524.20230071

• Biosystems Engineering • Previous Articles     Next Articles

Automatic segmentation and quantification system for Populus tomentosa roots based on improved PSPNet

ZHANG Pengchong1,2,3(), HAN Qiaoling1,2,3,4, XI Benye5, ZHENG Qiuyan1,2,3, ZHAO Yue1,2,3,4,*()   

  1. 1. School of Technology, Beijing Forestry University, Beijing 100083, China
    2. Beijing Lab of Urban and Rural Ecological Environment, Beijing 100083, China
    3. Research Center for Intelligent Forestry, Beijing 100083, China
    4. Key Lab of National Forestry and Grassland Administration for Forestry Equipment and Automation, Beijing 100083, China
    5. Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
  • Received:2023-01-16 Online:2024-02-25 Published:2024-03-05

Abstract:

In the minirhizotron images of Populus tomentosa roots, the root color is uneven, the root morphology is inconsistent, and there is little difference between the target and the background. Moreover, the existing root image processing software can not segment roots in batches. To solve the above problems, an automatic segmentation and quantification system for Populus tomentosa roots is proposed in the present study. This system includes two parts, namely, automatic root image segmentation part and root feature quantification part. Firstly, the PSEPNet (pyramid scene efficient parsing network) based on EPSANet50 is designed to realize the automatic segmentation of Populus tomentosa roots. Secondly, the skeleton thinning method is adopted to extract the pixel contour of the root center. Finally, mathematical statistics are used to realize the quantitative description of root number, root length and other characteristics. The test results show that the segmentation method used by the system has the best segmentation effect for minirhizotron images of Populus tomentosa roots, as the accuracy rate is 0.981 9, the recall rate is 0.884 9, the accuracy is 0.830 9 and the F1 score is 0.851 2. This system can also realize the quantification of root number, root length, root projection area and other characteristics to provide technical support and theoretical basis for the study of tree growth laws based on minirhizotron technology.

Key words: minirhizotron, Populus tomentosa roots, semantic segmentation, feature quantification

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