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Canopy photosynthesis instead of leaf photosynthesis is highly related to plant biomass and yield formation. Hence studying canopy photosynthesis and identifying parameters controlling canopy photosynthesis can help optimize agricultural management and crop yield potential. In this study, we first used multi-perspective two-dimensional imaging to perform three-dimensional point cloud reconstruction of rice plants. We developed a suite of pipelines to calculate plant height, leaf count, tiller count, and biomass, achieving \(R^2\) values of 91.8%, 95.9%, 82.3%, and 94.3%, respectively. We further used the ray tracing to simulate the light distribution and calculate the photosynthetic rate. Exploring the relationship between different phenotypes and photosynthetic rates, we found that canopy occupation volume (COV) was the most effective predictor of rice canopy photosynthesis rates, achieving an impressive \(R^2\) value of 92.1%. In summary, the method developed in this study can be used to support future agronomic and breeding research to improve canopy photosynthesis.

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3D Phenotyping of Canopy Occupation Volume as a Major Predictor for Canopy Photosynthesis in Rice (Oryza sativa L.)

  • 会议论文

  • 发表日期：

    2025年

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  • Jiaren Zhou,
  • Man Zhang,
  • Mengqi Zhang,
  • Minjuan Wang

摘要

Canopy photosynthesis instead of leaf photosynthesis is highly related to plant biomass and yield formation. Hence studying canopy photosynthesis and identifying parameters controlling canopy photosynthesis can help optimize agricultural management and crop yield potential. In this study, we first used multi-perspective two-dimensional imaging to perform three-dimensional point cloud reconstruction of rice plants. We developed a suite of pipelines to calculate plant height, leaf count, tiller count, and biomass, achieving \(R^2\) values of 91.8%, 95.9%, 82.3%, and 94.3%, respectively. We further used the ray tracing to simulate the light distribution and calculate the photosynthetic rate. Exploring the relationship between different phenotypes and photosynthetic rates, we found that canopy occupation volume (COV) was the most effective predictor of rice canopy photosynthesis rates, achieving an impressive \(R^2\) value of 92.1%. In summary, the method developed in this study can be used to support future agronomic and breeding research to improve canopy photosynthesis.