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

我们都依靠农业维持生计。与海鲜和牲畜相比，种植制度提供了主要的营养来源。种植制度的产量和生产力必须增长，以满足不断增长的人口的需求。一旦有了种子，成功的收获季节取决于水分。这有两个来源：灌溉和降水。灌溉用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《土壤和水资源保护法》的评估中，美国农业部将灌溉用水保护确定为国家需要。水分不足会引起压力，并对作物生长和产量产生不利影响。另一方面，过度灌溉会导致养分流失、土壤侵蚀和水资源浪费。农场还受到干旱、降水变化和生长季延长等不利影响的影响。拟议中的努力侧重于水管理和保护，是对农场经常遇到的逆风的适应。该项目致力于解决浇水过多(土壤侵蚀和养分径流)和浇水不足(不利作物产量和压力)的相互关联的方面，同时确保农业系统的可持续性和盈利性。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水分需求，并实施变量灌溉战略，以优化整个田地的作物产量。我们用有限数量的现场土壤水分传感器测量场地；这些现场观测与雷达和卫星的遥感数据相辅相成。这项工作包括设计基于深度神经网络(DNN)的新AI(人工智能)方法，以生成水需求预测。这些DNN在多模式、高维数据上运行，以识别田间不同地区的土壤水分亏缺和变异性。生成的预报考虑了作物、土壤类型、降水事件和作物生长阶段。该项目关闭了人工智能引导的网络物理系统中传感环境和驱动之间的环路。这些预测在基于博弈论的算法中被利用，以通过控制喷嘴和区域级别的浇水速度的处方计划来通知浇水臂的精确驱动。该算法对降水事件、预报中的不确定性和驱动开销具有自适应和响应能力。这项多方面的研究通过创新性地结合传感环境、算法博弈论、科学模型和领域科学以及AI/DNN来推进网络物理系统的科学。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.