Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2021, Vol. 3 ›› Issue (1): 51-62.doi: 10.12133/j.smartag.2021.3.1.202102-SA003

• Topic--Frontier Technology and Application of Agricultural Phenotype • Previous Articles     Next Articles

Cotton Phenotypic Trait Extraction Using Multi-Temporal Laser Point Clouds

YANG Xu1,2(), HU Songtao1, WANG Yinghua4, YANG Wanneng3,4, ZHAI Ruifang1()   

  1. 1.College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
    2.Shenzhen Fortune Trend Technology Co. , Ltd. , Wuhan 430070, China
    3.National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
    4.College of Plant Science & Technology, Huazhong Agricultural University, Wuhan 430070, China
  • Received:2021-02-01 Revised:2021-02-26 Online:2021-03-30 Published:2021-06-01
  • corresponding author: Ruifang ZHAI E-mail:yang_9325@163.com;rfzhai@mail.hzau.edu.cn


To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality. Therefore, the efficient, accurate, and non-destructive measurement of crop phenotypic traits, and the dynamic quantification of phenotypic traits are urgently needed for crop phenotypic research, and breeding as well as for modern agricultural development. In this study, cotton plants were taken as research objects, and the multi-temporal point cloud data of cotton plants were collected by using three-dimensional laser scanning technology. The multi-temporal point clouds of three cotton plants at four time points were collected. First, RANSAC algorithm was implemented for main stem extraction on the original point cloud data of cotton plants, then region growing based clustering was carried out for leaf segmentation. Plant height was estimated by calculating the end points of the segmented main stem. Leaf length and width measurements were conducted on the segmented leaf parts. In addition, the volume was also estimated through the convex hull of the original point cloud of plant cotton. Then, multi-temporal point clouds of plants were registered, and organ correspondence was constructed with the Hungarian method. Finally, dynamic quantification of phenotypic traits including plant volume, plant height, leaf length, leaf width, and leaf area were calculated and analyzed. The overall performance of the approaches achieved a matching rate through a series of experiments, and the traits extracted by using of point cloud showed high correlation with the manually measured ones. The relative error between plant height and manual measurement results did not exceed 1.0%. The estimated leaf length and width on point clouds were highly correlated with the manually measured ones, and the coefficient of determination was nearly 1.0. The proposed 3D phenotyping methodology can be introduced and used to other crops for phenotyping.

Key words: cotton phenotypic traits, LiDAR, stem extraction, leaf segmentation, point cloud registration, 3D phenotyping

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