LettuceP3D: A tool for analysing 3D phenotypes of individual lettuce plants

文献类型： 外文期刊

第一作者： Ge, Xiaofen

作者： Ge, Xiaofen;Guo, Xinyu;Ge, Xiaofen;Wu, Sheng;Wen, Weiliang;Xiao, Pengliang;Lu, Xianju;Liu, Haishen;Zhang, Minggang;Guo, Xinyu;Ge, Xiaofen;Wu, Sheng;Wen, Weiliang;Xiao, Pengliang;Lu, Xianju;Liu, Haishen;Zhang, Minggang;Guo, Xinyu;Wu, Sheng;Wen, Weiliang;Shen, Fei

作者机构：

关键词： Lettuce; Point cloud segmentation; Deep learning; Phenotypic analysis algorithm

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 251 卷

页码：

收录情况： SCI

摘要： Lettuce is one of the major raw vegetables in the world, with diverse species and large differences in morphological structures. Achieving automated, high-throughput acquisition and intelligent analysis of 3D lettuce phenotypes using advanced phenotyping techniques and equipment is of great significance. Based on the high- throughput phenotyping platform MVS-PhenoV2 installed in a plant imaging room, this study constructed a method for automated analysis of 3D phenotypes of lettuce around the needs of lettuce DUS (distinctiveness, uniformity, and stability) testing and feature digitisation. Aiming at the characteristics of lettuce leaves which are mostly curved, the point cloud segmentation model SoftGroup was improved, which can realise lettuce single plant segmentation and leaf segmentation with high accuracy. Additionally based on lettuce 3D point clouds, plant orientation correction algorithm, leaf hole completion algorithm, leaf vein extraction algorithm, and leaf margin extraction algorithm were proposed. Finally, a pipelined automated analysis software tool LettuceP3D was developed for automated analysis of lettuce 3D phenotypes, which can automatically analyse 16 phenotypic indicators related to lettuce plant (e.g. plant height, plant width, and compactness) and leaf (e.g. leaf length, leaf margin perimeter, and leaf margin undulation) phenotypic characteristics. The study was validated on seven types of lettuce: Butterhead, Crisphead, Looseleaf, Oakleaf, Romaines, Stem, and Wild Relatives. Results show that the mIoU for plant and pot semantic segmentation reaches 97.2%, and the AP for leaf instance segmentation reaches 86.7%. Through comparison with measured values, the average R2 of the algorithm exceeds 0.95. The software operates without manual interaction, processing single plant data in approximately 2s, which demonstrates a high processing efficiency. This phenotype analysis method proposed in this study is applicable for quantifying the morphological characteristics of lettuce in seven types, providing quantitative indicator data support for lettuce DUS testing, variety identification, and multi-omics studies.

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