SBIR Phase I: Models to predict soil and plant water status from continuous in-plant measurements

SBIR 第一阶段：通过连续的厂内测量来预测土壤和植物水分状况的模型

• 批准号: 2026205
• 负责人: Michael Santiago
• 金额: $ 25.55万
• 依托单位: FloraPulse Co
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026205&HistoricalAwards=false
• 关键词: SBIR; Phase; Models; predict; soil

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to help fruit and tree nut growers minimize environmental impacts and improve profitability. This project will use data from implantable microchip sensors that directly measure tree hydration to develop precision models of tree and vine needs for water. These models require no hardware installation and are packaged in a user-friendly format. They will automatically provide growers with affordable advice tailored to their field and crop, enabling accurate 24/7 water status data, forecasts, and recommendations for large-scale improvements in irrigation management of tree crops.This SBIR Phase I project will explore plant health via continuous variation of water status or drought stress within the tissues. This data stream will be used to build dynamical models of plant water stress. The project's technical aims are to: 1) Characterize the spatial (due to plant position in the field) and temporal variations; 2) Develop a framework for iterative development of predictive models of water stress dynamics from the single-plant to the whole-field scale; and 3) Develop a system optimized for industrial modeling of the spatial and temporal dynamics of water across the full orchard or vineyard, diagnostics of high- and low-performing cultivars, irrigation blocks, and decision support to optimize field design and crop management.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是帮助水果和坚果种植者最大限度地减少环境影响并提高盈利能力。该项目将使用直接测量树木水合作用的植入式微芯片传感器的数据来开发树木和藤蔓对水需求的精确模型。这些模型不需要安装硬件，并且以用户友好的格式打包。它们将自动为种植者提供适合其田地和作物的实惠建议，从而提供准确的 24/7 水状况数据、预测和建议，以大规模改善木本作物的灌溉管理。该 SBIR 第一阶段项目将通过以下方式探索植物健康：组织内水分状况或干旱胁迫的持续变化。该数据流将用于建立植物水分胁迫的动态模型。该项目的技术目标是： 1) 表征空间（由于植物在田间的位置）和时间变化； 2）开发从单株到全田尺度的水分胁迫动态预测模型迭代开发框架； 3) 开发一个优化的系统，用于对整个果园或葡萄园的水的空间和时间动态进行工业建模，对高低性能品种、灌溉区进行诊断，并提供决策支持，以优化田间设计和作物管理。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。

项目成果

Data-driven robust model predictive control framework for stem water potential regulation and irrigation in water management

• DOI: 10.1016/j.conengprac.2021.104841
• 发表时间: 2021-08
• 期刊: Control Engineering Practice
• 影响因子: 4.9
• 作者: Wei-Han Chen;Chao Shang;Siyu Zhu;K. Haldeman;M. Santiago;A. Stroock;F. You