High Throughput 3D Phenotyping of Canopy Occupation Volume as Major Predictor of Rice Canopy Photosynthesis

Abstract

Canopy photosynthesis, rather than leaf photosynthesis, is highly related to plant biomass and yield formation. Studying canopy photosynthesis and identifying the parameters that control it can help optimize agricultural management and achieve crop yield potential. Compared with traditional parameters, canopy occupation volume (COV) offers an integrative parameter on canopy architecture related to canopy photosynthetic rates. In this study, we developed a high-throughput method to derive COV for different rice varieties. We first used multi-perspective two-dimensional imaging to reconstruct three-dimensional point clouds of rice plants and developed a suite of pipelines to calculate plant height, leaf number, tiller number, and biomass, with R² values of 91.8%, 95.9%, 82.3%, and 94.3%, respectively. We further employed point cloud data to reconstruct the surfaces of rice plants and construct a virtual canopy model of the rice population. Light distribution was simulated using a ray-tracing algorithm and canopy photosynthetic rates were simulated via photosynthetic rate-incident light intensity curve fitting. Furthermore, we systematically explored the relationships between canopy phenotypes and photosynthetic rates, and found that COV was the most effective predictor of canopy photosynthesis, achieving an R² value of 92.1%. Adjustment in atmospheric transmittance showed that COV strongly correlated with canopy photosynthesis under different light conditions, with higher accuracy observed under diffuse light. Variations in planting density confirmed that this correlation remained strong at the community level. In summary, this study demonstrates that COV is closely linked to simulated canopy photosynthesis and the developed pipeline can support future agronomic and breeding research.