Development of new means for advanced analysis of crop plant shoot architectonics and other phenotype attributes

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To feed the rapidly growing human population, which is projected to be 9.7 billion by 2050, the improvement of food production has to be accelerated. It poses tremendous challenges to agriculture in the face of global environmental change. Plant shoot architecture is the 3D organization of above-ground parts of a plant. It has been proved that modification of plant architecture is a promising strategy to improve crop yield. The selection of new varieties with ideal plant architecture requires accurate and extensive characterization of phenotypic traits. In this program, soybean (Glycine max) will be used as the model plant to obtain 3D point clouds using a high-resolution terrestrial light detection and ranging (LiDAR) sensor. The resulting data will be used to develop novel analytical tools using artificial intelligence and computer vision for 3D comprehensive phenotyping of plant shoot architecture in a spatial and temporal manner. The tools can extract new advanced traits such as 3D organ distribution and topological pattern which cannot be obtained using current phenotyping methods from 2D images. This project has three significant aims. The first aim is to design 3D deep learning networks to segment individual organs in point clouds. Accurate segmentation of 3D plant architecture is a critical prerequisite for quantitative phenotyping of individual organs. The second aim is to characterize 3D plant architecture by reconstructing 3D plant models. This aim will: (1) study recovering missing parts in point clouds for individual organs due to occlusion problems and build quantitative structure models; (2) extract phenotypic traits of interest from the whole plant to organ levels. The third aim is to track spatial-temporal behaviors of plant architecture. The results of this program will provide plant breeders tools to increase the efficiency of plant breeding progress, and the tools will help plant scientists explore principles of how plant architecture grows and adapts to changing environments; Also, they will assist Canadian farmers (directly or through dedicated service providers) optimize crop management practice that will reduce water and energy use without compromising yield, increasing their profitability and better control the environmental footprint. The tools can be generalized to other crops such as maize after minor modification. The project represents a new interdisciplinary research integrating artificial intelligence and plant science and contributes to addressing several general 3D computer vision challenges such as 3D object reconstruction, segmentation, and 3D object tracking in point clouds. Educationally, the project can help educate and train computational plant science researchers who are critically needed in Canada. In the future, robot-assisted high throughput phenotyping systems combining with the tools will be developed for automatic analysis for plant shoot architectures under field conditions.