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Automatic acquisition, analysis and wilting measurement of cotton 3D phenotype based on point cloud
Biosystems Engineering ( IF 5.1 ) Pub Date : 2024-02-28 , DOI: 10.1016/j.biosystemseng.2024.02.010
Haoyuan Hao , Sheng Wu , YuanKun Li , Weiliang Wen , jiangchuan Fan , Yongjiang Zhang , Lvhan Zhuang , Longqin Xu , Hongxin Li , Xinyu Guo , Shuangyin Liu

This study constructed a high-throughput method for the acquisition and analysis of three-dimensional phenotypes of cotton, and proposes a method for evaluating the degree of wilting of cotton varieties based on phenotype. The upgraded version of the self-developed data acquisition platform MVS-Pheno V2 was used to continuously collect point cloud data. PointSegAt deep learning network model was used to establish plant stem and leaf segmentation and leaf overlap distinction models, realising the segmentation of cotton plant stems and leaves and the distinction of leaf overlap. In addition, an algorithm called "Active Boundary Segmentation" has been developed, which achieved automatic segmentation of overlapping cotton leaves. Based on point cloud technology, the automation of plant height, leaf count, and wilted leaf area based on voxels was realised, and a set of wilt measurement methods for cotton plants was designed. The results show that the PointSegAt model proposed has good performance in stem and leaf segmentation, with a segmentation accuracy of 0.995 and mean intersection over union of 0.924. In terms of single leaf segmentation, the average accuracy reached 0.95, and the average f1-score reached 0.94. Compared with manual measurements of plant height, leaf count, leaf area, and canopy area, the correlation coefficients were 0.99, 0.96, 0.90, and 0.99, respectively, and the root mean square errors were 0.01, 0.04, 0.19, and 0.02, respectively. Finally, the proposed method was used to perform wilting quantification experiments on two different varieties of cotton plants, and quantitative analysis of drought resistance of different varieties was conducted.

中文翻译:

基于点云的棉花3D表型自动采集、分析及萎蔫测量

本研究构建了棉花三维表型获取与分析的高通量方法,提出了基于表型评价棉花品种萎蔫程度的方法。采用自主研发的数据采集平台MVS-Pheno V2升级版,持续采集点云数据。利用PointSegAt深度学习网络模型建立植物茎叶分割和叶片重叠区分模型,实现棉花植物茎叶分割和叶片重叠区分。此外,还开发了一种名为“主动边界分割”的算法,实现了重叠棉叶的自动分割。基于点云技术,实现了基于体素的株高、叶数、枯萎叶面积的自动化测量,设计了一套棉株枯萎测量方法。结果表明,提出的PointSegAt模型在茎叶分割方面具有良好的性能,分割精度为0.995,平均交集比并集为0.924。在单叶分割方面,平均准确率达到0.95,平均f1-score达到0.94。与手工测量的株高、叶数、叶面积、冠层面积相比,相关系数分别为0.99、0.96、0.90、0.99,均方根误差分别为0.01、0.04、0.19、0.02 。最后利用该方法对两个不同品种的棉株进行萎蔫定量实验,对不同品种的抗旱性进行定量分析。
更新日期:2024-02-28
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