SBIR Phase I: A real-time precision nutrient analysis and management system for hydroponic farming operations

SBIR 第一阶段：用于水培农业作业的实时精确养分分析和管理系统

• 批准号: 2210046
• 负责人: Carlos Hayden
• 金额: $ 25.6万
• 依托单位: ENVONICS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210046&HistoricalAwards=false
• 关键词: SBIR; Phase; real; time; precision

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to promote the viability and sustainability of small-to-medium indoor, urban, and controlled environment agriculture (CEA) farms. As the global population grows to 10 billion by 2050, the agriculture industry will need to produce 70% more food using only 5% more land. Indoor farming can make a significant contribution to meet this demand sustainably. Indoor farmers are also seasonally and geographically independent, which means they can help meet demands for locally produced fresh foods and are protected from extreme weather events. These farms primarily use soilless growing methods, such as hydroponics, that currently suffer from critical needs for efficient and affordable methods to monitor and manage nutrients and water in order to be financially viable and environmentally sustainable. The proposed project provides an innovative solution for nutrient management in hydroponic farming, thereby lowering the costs, increasing the yield potential, and supporting the viability of such farms. By supporting the expansion of the national hydroponics industry, this project will increase the local production of and expand access to fresh produce.This SBIR Phase I project will develop a nutrient management system to provide CEA farmers with real-time information about the nutrients in the growth solution of their crops. The proposed solution will utilize ion-selective electrode (ISE) technology and a decision support system powered by machine learning (ML). This project will focus on the critically needed engineering and data analytics research and development to de-risk major technical challenges in the development of the nutrient management system, providing proof-of-feasibility. The key objectives of this project are to: 1) design a special chamber for the sensors to minimize the interference and increase accuracy, 2) validate the feasibility and accuracy of this new design in a greenhouse setting, 3) develop a predictive algorithm to automatically calibrate the sensors, and 4) measure and predict deficiencies in leafy greens production: collecting empirical evidence of nutrient deficiency to train ML models to identify, and ultimately, predict a deficiency prior to when it is observable.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.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是促进中小型室内，城市和受控环境农业（CEA）农场的可行性和可持续性。到2050年，全球人口将增长到100亿，农业将需要使用5%的土地生产70%以上的粮食。室内农业可以为可持续地满足这一需求做出重大贡献。室内农场主在季节和地理上也是独立的，这意味着他们可以帮助满足对当地生产的新鲜食品的需求，并免受极端天气事件的影响。这些农场主要使用无土栽培方法，如水培法，目前迫切需要有效和负担得起的方法来监测和管理养分和水，以便在经济上可行和环境上可持续。拟议的项目为水培农业的营养管理提供了一种创新的解决方案，从而降低了成本，提高了产量潜力，并支持了这些农场的生存能力。通过支持国家水培产业的发展，该项目将增加当地新鲜农产品的产量，并扩大新鲜农产品的获取。SBIR第一阶段项目将开发一个营养管理系统，为CEA农民提供作物生长液中营养物质的实时信息。 拟议的解决方案将利用离子选择性电极（伊势）技术和由机器学习（ML）驱动的决策支持系统。该项目将专注于急需的工程和数据分析研究和开发，以降低养分管理系统开发中的主要技术挑战风险，并提供可行性证明。该项目的主要目标是：1）为传感器设计一个特殊的腔室，以最大限度地减少干扰并提高准确性，2）在温室环境中验证这种新设计的可行性和准确性，3）开发一种预测算法来自动校准传感器，以及4）测量和预测绿叶蔬菜生产中的缺陷：收集营养素缺乏的经验证据，以训练ML模型来识别，并最终，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。