CIF21 DIBBs: PD: Cyberinfrastructure Tools for Precision Agriculture in the 21st Century

CIF21 DIBB：PD：21 世纪精准农业的网络基础设施工具

• 批准号: 1724843
• 负责人: Michela Taufer
• 金额: $ 50万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2018-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724843&HistoricalAwards=false
• 关键词: CIF21; DIBBs; PD; Cyberinfrastructure; Tools

This interdisciplinary project applies computer science approaches and computational resources to large multidimensional environmental datasets, and synthesizes this information into finer resolution, spatially explicit products that can be systematically analyzed with other variables. The main emphasis is ecoinformatics, a branch of informatics that analyzes ecological and environmental science variables such as information on landscapes, soils, climate, organisms, and ecosystems. The project focuses on synthesis/computational approaches for producing high-resolution soil moisture datasets, and the pilot application is precision agriculture. The effort combines analytical geospatial approaches, machine learning methods, and high performance computing (HPC) techniques to build cyberinfrastructure tools that can transform how ecoinformatics data is analyzed.The investigators build upon publicly available data collections (soil moisture datasets, soil properties datasets, and topography datasets) to develop: (1) tools based on machine-learning techniques to downscale coarse-grained data to fine-grained datasets of soil moisture information; (2) tools based on HPC techniques to estimate the degree of confidence and the probabilities associated with the temporal intervals within which soil-moisture-base changes, trends, and patterns occur; and (3) data- and user- interfaces integrating data preprocessing to deal with data heterogeneity and inaccuracy, containerized environments to assure portability, and modeling techniques to represent temporal and spatial patterns of soil moisture dynamics. The tools will inform precision agriculture through the generation and use of unique information on soil moisture for the coterminous United States. Accessibility for field practitioners (e.g., local soil moisture information) is made possible through lightweight virtualization, mobile devices, and web applications. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Earth Sciences within the NSF Directorate for Geosciences.

该跨学科项目将计算机科学方法和计算资源应用于大型的多维环境数据集，并将这些信息综合为更精细的分辨率，可以与其他变量系统地分析的空间显式产品。 主要重点是生态信息学，这是信息学的一个分支，分析了生态和环境科学变量，例如有关景观，土壤，气候，生物和生态系统的信息。 该项目着重于生产高分辨率土壤水分数据集的合成/计算方法，而试验应用是精确的农业。 The effort combines analytical geospatial approaches, machine learning methods, and high performance computing (HPC) techniques to build cyberinfrastructure tools that can transform how ecoinformatics data is analyzed.The investigators build upon publicly available data collections (soil moisture datasets, soil properties datasets, and topography datasets) to develop: (1) tools based on machine-learning techniques to downscale coarse-grained data to fine-grained土壤水分信息的数据集； （2）基于HPC技术的工具，以估算与土壤碱基碱的变化，趋势和模式发生的时间间隔相关的置信程度和概率； （3）集成数据预处理以处理数据异质性和不准确性，容器化环境以确保可移植性以及建模技术以表示土壤水分动态的时间和空间模式的建模技术。这些工具将通过生成和使用有关土壤水分的独特信息来告知精确农业。 通过轻巧的虚拟化，移动设备和Web应用程序，实现现场从业人员的可访问性（例如，当地的土壤水分信息）。高级网络基础设施办公室的裁决由NSF地球科学局内的地球科学司共同支持。