Estimation of Leaf Angle Distribution Based on Statistical Properties of Leaf Shading Distribution

Leaf angle distribution is an important phenotype parameter that is related to photosynthesis. Thanks to the recent advent of drones and high-resolution imaging devices, leaf-scale aerial images with high spectral and spatial resolution are available. This work is the first attempt to utilize a single leaf-scale image to differentiate plants with different leaf angle distribution. First, assuming that a rice leaf surface resembles a section of a hemiellipsoid surface, a collection of rice leaf surfaces is approximated by a hemiellipsoid surface. Time-series of shading distributions on the hemiellipsoids with different structural parameters under different direct sunlight directions are generated. By investigating the statistical properties, i.e., skewness, kurtosis and the most probable intensity, of the frequencies of the simulated shading intensity that well-differentiate hemiellipsoids with different structural parameters, we identified an appropriate time slot, i.e., 11: 00-12:30, for image acquisitions. Then, time-series leaf-scale images and depth maps of rice plants with/without silicate fertilizer under sunlight were collected. Based on the depth maps, it was confirmed that silicate fertilizer dosed leaves are more upright than leaves from non treated plants. It was demonstrated that 89% and 100% of kurtosis and the most probable intensity of the leaf-scale images during the appropriate time slot showed consistent relations with the simulations, which indicates that the proposed method is useful to distinguish different leaf angle distributions based on the frequency of shading intensity of rice leaf images.