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

这个小企业创新研究（SBIR）第一阶段项目的广泛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，农业将只需要增加5%的土地就能生产出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。室内农民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种植方法，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营养和水，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。通过支持全国水培产业的发展，该项目将增加当地的生鲜农产品产量并扩大获取渠道。该SBIR一期项目将开发一个营养管理系统，为CEA农民提供有关其作物生长溶液中营养成分的实时信息。提出的解决方案将利用离子选择电极（ISE）技术和由机器学习（ML）驱动的决策支持系统。该项目将重点关注急需的工程和数据分析研究与开发，以降低营养管理系统开发中的重大技术挑战的风险，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室，以尽量减少干扰并提高精度；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；4)测量和预测绿叶蔬菜生产中的缺陷。收集营养缺乏的经验证据来训练ML模型，以识别并最终在可观察到的情况之前预测营养缺乏。该奖项反映了美国国家科学基金会的法定使命，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 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.