I-Corps: Transformational Nutrient Curation and Management for Hydroponic Systems

I-Corps：水培系统的变革性养分培育和管理

• 批准号: 1833294
• 负责人: John Blaho
• 金额: $ 5万
• 依托单位: CUNY City University of New York
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1833294&HistoricalAwards=false
• 关键词: Corps; Transformational; Nutrient; Curation; Management

The broader impact/commercial potential of this I-Corps project focuses on the development of a data driven approach combined with automation equipment to optimize plant nutrition in real time. The project goal is the development of an efficient and practical machine learning system for nutrient curation and management that may revolutionize commercial farming. Current technologies on the market lack adequate performance specifications and are not cost effective for commercial applications. As a result of changing climate conditions and economic factors, many farms struggle to maintain profitability due to new operational challenges and risks. Worldwide, arable outdoor farmland is dramatically being reduced due to changing climate conditions. The proposed I-Corps project will initially explore the warehouse farm sector that grows produce indoors close to the point where this produce is consumed. Direct beneficiaries of this project include the US agriculture industry, as well as those responsible for national food security. Development of technologies to assist new forms of farming are now required to ensure access to fresh and more nutritionally dense produce in the decades ahead. This I-Corps project is based on the premise that the rational addition of nutrients to hydroponic systems based on real time feedback optimizes plant growth. The core technology includes an automated nutrient dosing and pH balancing system which collects data and acts on environmental data. This system consists of a sensor module monitoring hydroponic nutrient reservoir conditions and pump modules, combined with a cloud based data storage and processing component that adjusts chemical conditions as needed. The driving algorithms utilize analytical techniques that model nutrient factor usage and predicts their effects on cellular gene expression and overall plant health. Together, the hardware and software components derive a variety of plant growth metrics that will form the basis for a larger framework of environmental controls to dictate optimal plant growth.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i-Corps项目的更广泛的影响/商业潜力集中在开发一种数据驱动的方法，结合自动化设备来实时优化植物营养。该项目的目标是开发一种高效而实用的机器学习系统，用于营养管理和管理，可能会给商业农业带来革命性的变化。目前市场上的技术缺乏足够的性能规格，并且对于商业应用来说成本效益不高。由于气候条件和经济因素的变化，由于新的经营挑战和风险，许多农场难以保持盈利。在世界范围内，由于气候条件的变化，可耕种的户外农田正在急剧减少。拟议的i-Corps项目最初将探索仓库农场部门，该部门在室内种植农产品，接近农产品的消费点。该项目的直接受益者包括美国农业，以及那些负责国家粮食安全的人。现在需要开发技术来协助新的耕作形式，以确保在今后几十年里能够获得新鲜和营养更密集的农产品。这个i-Corps项目是基于这样的前提，即基于实时反馈向水培系统合理添加养分可以优化植物生长。核心技术包括自动营养配给和pH平衡系统，该系统收集数据并对环境数据采取行动。该系统由监测水培营养库条件的传感器模块和泵模块组成，并结合基于云的数据存储和处理组件，根据需要调整化学条件。驱动算法利用分析技术对营养因子的使用进行建模，并预测它们对细胞基因表达和整体植物健康的影响。硬件和软件组件一起得出了各种植物生长指标，这些指标将构成更大的环境控制框架的基础，以指示最佳植物生长。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。