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

Canopy photosynthesis, rather than leaf photosynthesis is highly related to plant biomass and yield formation. Studying canopy photosynthesis and identifying parameters that control it can help optimize agricultural management and realize 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 cultivars. We first used multi-perspective two-dimensional imaging to perform three-dimensional point cloud reconstruction of rice plants, and developed a suite of pipelines to calculate plant height, leaf count, tiller count, and biomass, with R 2 values of 91.8%, 95.9%, 82.3%, and 94.3%, respectively. We further employed point cloud data to reconstruct the surface of rice plants and construct a virtual canopy model of the rice population. Light distribution was simulated using a ray tracing algorithm, followed by calculation of simulated canopy photosynthetic rates via photosynthetic rate (A)-incident light intensity (Q) 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 R 2 value of 92.1%. Adjusting atmospheric transmittance showed that COV strongly correlates with canopy photosynthesis under different light conditions, with higher accuracy observed under diffuse light. Varying planting density confirmed that this correlation remains strong at the community level. In summary, this study demonstrates that COV is closely linked to simulated canopy photosynthesis and that the developed pipeline can support future agronomic and breeding research.