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.

这个小型企业创新研究(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将对环境的影响降至最低，并提高盈利能力。该项目将使用植入式微型芯片传感器的数据，这些传感器直接测量树木的水合作用，以开发树木和藤本植物对水分需求的精确模型。这些型号不需要安装硬件，并以用户友好的格式打包。他们将自动为种植者提供适合他们的田地和作物的负担得起的建议，实现24/7准确的水分状况数据、预测和建议，以大规模改善树木作物的灌溉管理。这个SBIR第一阶段项目将通过持续变化水分状况或组织内的干旱胁迫来探索植物健康。这些数据流将被用来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(由于植物在田间的位置)和时间的变化：2)开发从单植物尺度到整个农田尺度的水分胁迫动态预测模型的迭代开发框架；以及3)开发一个优化的系统，用于对整个果园或葡萄园的水的时空动态进行工业建模，诊断高和低表现的品种、灌溉区块，并为优化田地设计和作物管理提供决策支持。该奖项反映了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。

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