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

开发用于高级分析作物芽结构和其他表型属性的新方法

• 批准号: RGPIN-2021-03410
• 负责人: Sun, Shangpeng
• 金额: $ 1.89万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=746296
• 关键词: Development; new; means; advanced; analysis

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.

为了养活快速增长的人口（预计到 2050 年将达到 97 亿），必须加快粮食生产的改善。面对全球环境变化，它给农业带来了巨大挑战。植物芽结构是植物地上部分的 3D 组织。事实证明，改变植物结构是提高作物产量的一种有前景的策略。选择具有理想植物结构的新品种需要对表型性状进行准确和广泛的表征。在此计划中，大豆 (Glycine max) 将用作模型植物，使用高分辨率地面光探测和测距 (LiDAR) 传感器获取 3D 点云。所得数据将用于开发新颖的分析工具，利用人工智能和计算机视觉以空间和时间方式对植物芽结构进行 3D 综合表型分析。这些工具可以提取新的高级特征，例如 3D 器官分布和拓扑模式，这是使用当前的表型分析方法无法从 2D 图像中获得的。该项目有三个重要目标。第一个目标是设计 3D 深度学习网络来分割点云中的各个器官。 3D 植物结构的准确分割是单个器官定量表型的关键先决条件。第二个目标是通过重建 3D 植物模型来表征 3D 植物结构。该目标将：（1）研究恢复单个器官点云中由于遮挡问题而缺失的部分，并建立定量结构模型； (2)从整株植物到器官水平提取感兴趣的表型性状。第三个目标是跟踪植物结构的时空行为。该计划的成果将为植物育种者提供提高植物育种进展效率的工具，这些工具将帮助植物科学家探索植物结构如何生长和适应不断变化的环境的原理；此外，他们还将协助加拿大农民（直接或通过专门的服务提供商）优化作物管理实践，在不影响产量的情况下减少水和能源的使用，提高盈利能力并更好地控制环境足迹。经过细微修改后，这些工具可以推广到其他作物，例如玉米。该项目代表了人工智能和植物科学相结合的一项新的跨学科研究，有助于解决一些常见的 3D 计算机视觉挑战，例如点云中的 3D 对象重建、分割和 3D 对象跟踪。在教育方面，该项目可以帮助教育和培训加拿大急需的计算植物科学研究人员。未来，将开发机器人辅助的高通量表型系统与工具相结合，用于田间条件下植物芽结构的自动分析。