CAREER: From Underground to Space: An AI Infrastructure for Multiscale 3D Crop Modeling and Assessment

职业：从地下到太空：用于多尺度 3D 作物建模和评估的 AI 基础设施

• 批准号: 2340882
• 负责人: Guoyu Lu
• 金额: $ 54.99万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-06-01 至 2029-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2340882&HistoricalAwards=false
• 关键词: CAREER; Underground; Space; AI; Infrastructure

A crop's traits and 3D structure (the shape and architecture of plants, including both above- and below-ground parts) are the attributes that chiefly influence crop growth and yield and provide critical evidence for plant phenotyping (the characterization assessment of plant traits). Crop yield predictions can be made by assessing 3D plant structures using crop sensing methods. However, crop sensing results at different scales are usually analyzed in isolation, which overlooks essential connections. Moreover, while root systems play a central role in plant functions, current methods mainly assess crops based on above-ground crop structure due to the difficulty of accessing roots. Current methods use satellites for remote sensing and drones for local sensing, enabling crop assessment at varying scales; however, it is difficult to integrate these observations effectively, and the information stream is formidable. The overarching objective of this project is to develop a novel AI infrastructure to integrate these observations to model and assess 3D crop structures at multiple scales and enhance below-ground sensing capabilities. Using this infrastructure, 3D crop structures can be estimated accurately at the individual, farm, and satellite scales, facilitating crop assessment and yield prediction. The project dramatically enhances and accelerates the ability of growers and agronomists to assess critical crop field structural variation for both above- and below-ground components, enabling large-scale crop management. This project also benefits students, from the high school to the Ph.D. level, by applying multi-scale 3D models of above- and below-ground crop structures to immersive education methods (Virtual Reality (VR), Augmented Reality (AR), and online learning), which are well-suited to solving the challenges of distance learning, especially for subjects like agriculture requiring field study. The multi-scale sensing system is also capable of estimating 3D landscape structures and large-scale crop structures and can be utilized in other areas, such as Arctic Sea ice modeling, forestry, and climate change studies.This project aims to connect a plant’s structural phenotypes below- and above- ground and link in-situ measurements to satellite sensing data, thus enabling non-destructive crop root sensing and root-system status estimation based on observation of plant growth above-ground while at the same time empowering satellite images to assess these factors to furnish more local and detailed information. This project establishes a method for 3D crop sensing of individual plants, crop fields, and satellite regions to provide multi-scale crop structural evidence for crop assessment and yield prediction. This project also develops a novel AI neural network to sense root structures and predict traits based on sensing above-ground plant structures. This project investigates methods for satellite-based 3D sensing and nondestructive below-ground root sensing. Novel AI infrastructures are explored to address critical issues in computer vision and remote sensing, efficient integration of multi-scale sensing, 3D structure prediction, and spatial-temporal 4D inference. Such an approach can lower the ceiling for operational adoption of satellite and in-situ imagery assessments, based on a scientifically underpinned, multi-scale, 3D assessment workflow. In addition to its essential and practical implications for agriculture professionals, this project also explores novel AI solutions within computer vision and remote sensing. Crop structures are highly diverse, complicated, and changing phenomena. Therefore, agriculture presents an ideal research domain for investigating novel AI methods. This research advances AI by 1) largely improving the fusion effectiveness of various remote sensing modalities from sensors mounted on different devices, 2) significantly enhancing the learning capability by connecting sensing outputs expressed in multiple scales, 3) enabling 3D structure prediction for objects across different domains, and 4) providing future status prediction based on 4D spatial-temporal neural networks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

农作物的性状和3D结构（包括地上和地下部分的植物的形状和建筑）是主要影响农作物生长并产生产量并为植物表型的关键证据（植物特征的表征评估）的属性。作物产量预测可以通过使用作物敏感性方法评估3D植物结构来做出预测。但是，通常分别分析以不同尺度的作物敏感性结果，这忽略了基本的联系。此外，尽管根系在植物功能中起着核心作用，但由于难以进入根部，目前的方法主要根据地上作物结构评估农作物。当前的方法使用卫星来远程灵敏度和无人机来实现局部灵敏度，从而使作物评估在不同的尺度上。但是，很难有效地集成这些观察结果，并且信息流很大程度上是可怕的。该项目的总体目的是开发一种新型的AI基础架构，以将这些观测值整合到模拟和评估3D作物结构的多个尺度上，并增强地下灵敏度能力。使用此基础设施，可以在个人，农场和卫星量表上准确估算3D作物结构，从而支持作物评估和产量预测。该项目极大地增强并加速了种植者和农艺学家评估地上和地下组件的关键作物田间结构变化的能力，从而实现了大规模的作物管理。该项目也使从高中到博士学位的学生有益。级别，通过将上层和低于地面作物结构的多尺度3D模型应用于身临其境的教育方法（虚拟现实（VR），增强现实（AR）和在线学习），这些模型非常适合解决远距离学习的挑战，尤其是对于像同意实地研究这样的主题。多尺度的敏感性系统还能够估算3D景观结构和大规模的作物结构，并且可以在其他领域（例如北极海冰建模，林业和气候变化研究）中使用。此项目旨在将植物的结构表型在地下，地下和链接到智能的固定性启动式启动数据的启动，从而构成了智能的数据，从而将其连接起来，从而启用了智能的数据，以便启动智能的数据，以便于启动式态度，以便于启用智能传感。估计基于对植物生长的观察，同时赋予卫星图像的能力，以评估这些因素以提供更多的本地和详细信息。该项目为单个植物，作物场和卫星区域的3D作物敏感性建立了一种方法，以提供多尺度的农作物结构证据，以评估作物评估和产量预测。该项目还开发了一种新型的AI神经元网络，以感知根部结构并基于地面植物结构的灵敏度来预测性状。该项目研究了基于卫星的3D灵敏度和无损地下根部感测的方法。探索了新型的AI基础架构，以解决计算机视觉和遥远灵敏度的关键问题，多尺度传感器，3D结构预测和时空4D推断的有效整合。这种方法可以降低天花板，以基于科学基础的多尺度，3D评估工作流程的卫星和原位图像评估。除了它对农业专业人员的基本和实际含义外，该项目还探讨了计算机视觉和遥远敏感性中的新型AI解决方案。作物结构是高度多样，复杂且变化的现象。因此，农业提出了研究新型AI方法的理想研究领域。 This research advances AI by 1) largely improving the fusion effectiveness of various remote sensitivity modalities from sensors mounted on different devices, 2) significantly enhancing the learning capability by connecting sensitivity outputs expressed in multiple scales, 3) enabling 3D structure prediction for objects across different domains, and 4) providing future status prediction based on 4D spatial-temporal neural networks.This award reflects NSF's statutory mission并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估值得支持的。