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

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 railbased 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.

Automated summary

This study collected high-throughput, time-series raw data of field maize populations using a field rail-based phenotyping platform with LiDAR and an RGB camera. The orthorectified images and LiDAR point clouds were aligned using the direct linear transformation algorithm, and time-series point clouds were registered using time-series image guidance. The cloth simulation filter algorithm was then used to remove ground points, and individual plants and plant organs were segmented from the maize population. The plant heights obtained using the multi-source fusion data were highly correlated with manual measurements (R2 = 0.98), demonstrating the effectiveness of multi-source data fusion in improving the accuracy of time series phenotype extraction.