Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

Yinglun Li; Weiliang Wen; Jiangchuan Fan; Wenbo Gou; Shenghao Gu; Xianju Lu; Zetao Yu; Xiaodong Wang; Xinyu Guo

doi:10.34133/plantphenomics.0043

Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform

Plant Phenomics. 2023 Apr 20:5:0043. doi: 10.34133/plantphenomics.0043. eCollection 2023.

Authors

Yinglun Li^{1

2}, Weiliang Wen^{1

2}, Jiangchuan Fan^{1

2}, Wenbo Gou^{1

2}, Shenghao Gu^{1

2}, Xianju Lu^{1

2}, Zetao Yu², Xiaodong Wang², Xinyu Guo^{1

2}

Affiliations

¹ Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
² Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.

Abstract

The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (R² = 0.98), and the accuracy was higher than only using one source point cloud data (R² = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.