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

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，以真实的时间优化植物营养。 该项目的目标是开发一种高效实用的机器学习系统，用于营养物的管理和管理，这可能会彻底改变商业农业。 目前市场上的技术缺乏足够的性能规格，并且对于商业应用而言不具有成本效益。 由于气候条件和经济因素的变化，许多农场面临新的运营挑战和风险，难以维持盈利能力。 在世界范围内，由于气候条件的变化，可耕地的户外农田正在急剧减少。 拟议的I-Corps项目将首先探索仓库农场部门，该部门在室内靠近消费点种植农产品。 该项目的直接受益者包括美国农业以及负责国家粮食安全的部门。 现在需要开发技术，协助新的农业形式，以确保在未来几十年获得新鲜和营养更丰富的农产品。这个I-Corps项目的前提是，根据真实的时间反馈，向水培系统合理添加营养物质，优化植物生长。 其核心技术包括自动化营养物投加和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，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来模拟营养因子的使用，并预测它们对细胞基因表达和整体植物健康的影响。 硬件和软件组件共同推导出各种植物生长指标，这些指标将构成更大的环境控制框架的基础，以指导最佳植物生长。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.