Rapid methods for plant phenotyping are a growing need in agricultural research to help accelerate improvements in crop performance in order to facilitate more efficient utilization of plant genome sequences and the corresponding advancements in associated methods of genetic improvement. Manual plant phenotyping is time-consuming, laborious, frequently subjective, and often destructive. There is a need for building field-deployable systems with advanced sensors that have both high-speed and high-performance for plant phenotype processing.

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 This study reports on the design and performance of a new 3D computer vision-based plant phenotyping technology that utilizes 3D stereovision from many different viewing angles. The research presents new knowledge used to facilitate the determination of the best viewing angles for 3D reconstruction of plants.  A full 3D reconstruction system for plants is introduced that utilizes 16 high-resolution color stereovision cameras mounted on an arc-shaped superstructure designed for in-field use. The system incorporates both unique hardware features (including multiple cameras per arc and structured illumination to enhance the visual texture of plant surfaces) and software algorithms (including 3D feature extraction of plant height, number of leaves, leaf area, and plant biomass). Results demonstrate the ability to reconstruct complete 3D models of the plants growing in the natural outdoor environment of a farm.  The system allows photo-realistic plant models to be created from an optimum (i.e. minimum) number of digital color cameras positioned at different viewing angels. Experimental results of comparisons between different sets of viewing angles reveal that the top views are most advantageous for small plants while the side views provide greater information content for larger plants, where top views are detrimental in estimating plant height due to plant main stem occlusion by top leaves.