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3D time-series phenotyping of lettuce in greenhouses

文献类型： 外文期刊

作者： Ma, Hanyu 1 ; Wen, Weiliang 1 ; Gou, Wenbo 1 ; Lu, Xianju 3 ; Fan, Jiangchuan 1 ; Zhang, Minggang 3 ; Liang, Yuqiang 3 ; Gu, Shenghao 1 ; Guo, Xinyu 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Shanxi Agr Univ, Coll Agr, Taigu 030801, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

关键词： Time-series; 3D phenotyping; Rail-driven phenotyping platform; Lettuce; Greenhouse

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：5.3； 五年影响因子：5.9 ）

ISSN： 1537-5110

年卷期： 2025 年 250 卷

页码：

收录情况： SCI

摘要： Monitoring the growth dynamics of plants in three-dimensional (3D) space is one of the most fundamental data acquisition requirements for plant breeding and cultivation. The rapid development of high-throughput plant phenotyping platforms (HTPPP) makes it possible to obtain big data in plant phenomics. However, how to extract phenotypes from the raw phenotyping data to obtain the agronomic indicators demanded by agronomists has become an urgent issue. In this study, time-series point clouds of potted lettuce plants were generated via multiview stereo (MVS) method using top-view Red, Green, Blue (RGB) images acquired by a rail-driven HTPPP in a greenhouse. A time-series point cloud registration method was proposed by extracting pots as features, and daily population-individual plant point cloud segmentation was achieved based on the registration information and contrasted with two other different segmentation methods. Then vegetation and pot was segmented using the random forest (RF). Finally, the phenotypes including plant height, crown width, and convex hull volume of each plant were extracted. The results show that the average mean intersection over union (mIoU), mean precision (mPr), mean recall (mRe), and mean F1-score (mF1) of the population-individual plant segmentation were 71.86%, 97.38%, 86.08%, and 91.02%, respectively. The vegetation-pot point cloud segmentation achieved an accuracy of 98.81%. The averaged coefficient of determination (R2) for the extracted plant height and crown width were 0.79 and 0.60, respectively, with the averaged root mean square error (RMSE) being 0.05 m and 0.03 m, respectively. The accuracy of plant height was significantly higher than that of PlantEye. The extracted phenotypes can be used to quantitatively differentiate the growth dynamics of different sub-populations of lettuce plants. This study presents an automated solution for extracting time-series 3D phenotypes under HTPPP in a greenhouse. It provides crucial technological support for efficient phenotype acquisition in plant breeding and cultivation.

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