3D model processing for high throughput phenotype extraction – the case of corn

High resolution RGB imagery collected using a UAV and a handheld camera was used with structure from motion to reconstruct 3D canopies of small groups of corn plants. A methodology for the automated extraction of phenotypic characteristics of individual plants is presented based on these 3D reconstructed canopies. Such information can enhance the evaluation of crop traits and provide accurate and frequent statistics for in-season assessment of their changes with growth stage. Industries that target yield optimization and crop hybrid production can benefit greatly from this approach. The use of 3D models provides elevated information content, when compared to alternative planar methods, mainly due to the alleviation of leaf occlusions. High resolution images of corn stalks are collected and used to obtain 3D models for individual plants. Based on those extracted 3D point clouds, the calculation of phenotypic characteristics are obtained, such as the number of plants in an area, the Leaf Area Index (LAI), the individual and average plant height, the individual leaf length, the location and the angles of leaves with respect to the stem. An experimental validation using both artificial corn plants emulating real world scenarios and real corn plants in different growth stages, supports the accuracy of the proposed methodology.Our experiments conclude that phenotypic characteristics of individual plants can be extracted automatically with high accuracy based on a 3D model. The results include the individual plant segmentation and counting from a given 3D reconstructed field scene with 88.1% accuracy, the Leaf Area Index (LAI) estimation with 92.5% accuracy, the individual plant height computation with 89.2% accuracy, the leaf length extraction with 74.8% accuracy, the measurement of angles between leaves and stems, and the distance between the leaves of the same plant. We interpret the last two variables qualitatively to show that the method can show the trend of the angles to change with respect to the leaf position on the stem as the crops grow.