High-throughput volumetric reconstruction for 3D wheat plant architecture studies

Abstract

For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08[Formula: see text]cm with an average plant height of 40.07[Formula: see text]cm) and 10.06% (1.41[Formula: see text]g with an average plant fresh weight of 14.06[Formula: see text]g), respectively. The root mean square error (RMSE) was 1.37[Formula: see text]cm and 1.79[Formula: see text]g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20[Formula: see text]s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform.