CPS: Medium: Making Every Drop Count: Accounting for Spatiotemporal Variability of Water Needs for Proactive Scheduling of Variable Rate Irrigation Systems

CPS：中：让每一滴水都发挥作用：考虑用水需求的时空变化，主动调度可变速率灌溉系统

• 批准号: 2312319
• 负责人: Sangmi Pallickara
• 金额: $ 119.98万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312319&HistoricalAwards=false
• 关键词: CPS; Medium; Making; Every; Drop

We all depend on agriculture for sustenance. When compared to seafood and livestock, cropping systems provide the primary source of nutrition. Yields and productivity of cropping systems must grow to meet the demands of a growing population. Once seeds are available, a successful cropping season is determined by water. There are two sources for this: irrigation and precipitation. Irrigation water is a major input to agriculture, especially in semi-arid and arid regions. In a recent appraisal for the Soil and Water Resources Conservation Act, the USDA identified irrigation water conservation as a national need. Under-watering induces stresses and adversely impacts both crop growth and yields. Over-watering, on the other hand, leads to nutrient runoff, soil erosion, and water waste. Farms are also impacted by the adverse effects of droughts, variability in precipitation, and lengthening of the growing season. The proposed effort with its emphasis on water management and conservation represents an adaptation to the head winds often encountered at farms. The effort addresses the interrelated aspects of over-watering (soil erosion and nutrient runoff) and underwatering (adverse crop yields and stress) while ensuring sustainability and profitability of agricultural systems.The overarching objective of this project is to develop an end-to-end cyber-physical intelligence system that forecasts space-time crop water needs in a given field and implements variable rate irrigation strategies to optimize crop yield throughout the field. We instrument the field with a limited number of in-situ soil moisture content sensors; these in situ observations are complemented with remotely sensed data from radars and satellites. The effort includes design of novel AI (Artificial Intelligence) methods based on deep neural networks (DNN) to generate forecasts of water needs. These DNNs operate on multimodal, high-dimensional data to identify soil moisture deficits and variability in different parts of the field. The generated forecasts account for crop, soil type, precipitation events, and the crop growing phase. The project closes the loop between the sensing environment and actuation within the AI-guided cyber physical system. These projections are leveraged within a game theory based algorithm to inform precise actuations of the watering arm with prescription plans that control watering rates at the nozzle and zone level. The algorithm is adaptive and responsive to precipitation events, uncertainty in the forecasts, and the actuation overheads. This multifaceted research advances the science of cyber-physical systems by innovatively combining sensing environments, algorithmic game theory, scientific models and domain-science, and AI/DNNs.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.

我们都依赖农业维持生计。与海鲜和牲畜相比，种植系统提供了主要的营养来源。耕作系统的产量和生产力必须增长才能满足不断增长的人口的需求。一旦获得种子，成功的种植季节就取决于水。有两个来源：灌溉和降水。灌溉水是农业的主要投入，特别是在半干旱和干旱地区。在最近对《水土资源保护法》的评估中，美国农业部将灌溉节水确定为国家需求。浇水不足会引起压力并对作物生长和产量产生不利影响。另一方面，过度浇水会导致养分流失、土壤侵蚀和水资源浪费。农场还受到干旱、降水变化和生长季节延长的不利影响。拟议的工作重点是水管理和保护，代表了对农场经常遇到的逆风的适应。这项工作解决了过度浇水（土壤侵蚀和养分径流）和水下浇水（作物产量不利和压力）的相互关联问题，同时确保农业系统的可持续性和盈利能力。该项目的首要目标是开发一个端到端的网络物理智能系统，预测给定田地的时空作物用水需求，并实施可变速率灌溉策略，以优化整个田地的作物产量。我们在现场安装了数量有限的现场土壤湿度传感器；这些实地观测得到雷达和卫星遥感数据的补充。这项工作包括设计基于深度神经网络（DNN）的新型人工智能（AI）方法来生成用水需求预测。这些 DNN 对多模态、高维数据进行操作，以识别田地不同部分的土壤水分不足和变异性。生成的预测考虑了作物、土壤类型、降水事件和作物生长阶段。该项目闭合了人工智能引导的网络物理系统内的传感环境和驱动之间的循环。这些预测在基于博弈论的算法中得到利用，通过控制喷嘴和区域水平浇水速率的处方计划来通知浇水臂的精确驱动。该算法对降水事件、预测的不确定性和驱动开销具有适应性和响应性。这项多方面的研究通过创新地结合传感环境、算法博弈论、科学模型和领域科学以及 AI/DNN，推进了网络物理系统科学。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。