Harnessing AI-powered big data techniques for 3D plant architecture phenotyping and growth pattern modeling

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The continuous rapid growth in the global population and climate change is resulting in tremendous challenges for agricultural production systems. We have to speed up the breeding process to produce new crop varieties adapted to changing environments to ensure food security. The genomic revolution has provided unprecedented power to engineer new and advanced crop cultivars with gene combinations in plant breeding and selection programs. However, it is still laborious, expensive, and imprecise to relate genomic information to phenotypic information. High throughput plant phenotyping technologies that can rapidly and repeatedly measure phenotypic crop parameters are a major bottleneck in plant breeding programs. Over the past decade, 2D imaging processing technologies have been widely applied for plant phenotyping. However, the methods are hard to characterize 3D phenotypic traits. Converting 3D data into meaningful phenotypic information remains a bottleneck. In this program, we propose to develop novel AI-powered big data analytics technology to characterize and model plant shoot architectures and growth patterns from organ to whole plant scales in 3D.Chickpea (Cicer arietinum) will be used as the model plant in this program. A population of 20 varieties adapted to Quebec/Canadian conditions will be selected from a panel of 500 genetically broad chickpea accessions; For each variety, we will plant three repetitions in a greenhouse at McGill University. A low-cost multi-view photogrammetry system will be developed to scan the 60 plants at 20 developmental time points to build a high-resolution point cloud sequence dataset. Then, a novel labeling-efficient 3D deep learning network will be developed for an end-to-end instance segmentation of individual plants. We aim to use only around 0.5% points to be labeled for the segmentation model training. Also, 3D phenotypic traits at both organ and whole plant levels will be extracted. In addition, a dynamic point cloud modeling framework will be developed to characterize plant architecture spatio-temporal growth patterns. We will validate our methods and gained insights by conducting trials of the breeding and development of elite chickpea lines at the Emile A. Lods farm on the Macdonald campus of McGill University. Overall, the successful implementation of the project can accelerate plant breeding process and enhance the understanding of principles for how plants adapt to changing environments. Also, the developed point cloud dataset can help close the data gaps, enabling a broad set of new research and applications. Educationally, we will help train interdisciplinary HQP who are critically needed in Canada and globally for computational plant science.