SBIR Phase II: Intelligent modular vertical farming system

小型企业创新研究(SBIR)第二阶段项目的更广泛影响/商业潜力是改善垂直农场。全球对粮食安全的威胁，以及应对不可预测的气候条件的需要，为先进的精准农业和垂直城市农业打开了大门。全球人口迅速增长，对有限耕地的农业产量的需求推动了对垂直耕作和扩大这种可持续作物种植方法的新技术的需求。拟议的系统将通过减少种植农作物所需的水、化肥和杀虫剂来减轻环境负担。这些微型农场将通过提供可靠、可持续的食品体系以及新鲜、健康和环保的农产品来提高农业盈利能力。这在提高产量和营养密度的同时，减少了食物浪费和食物供应链对环境的影响。这个SBIR第二阶段项目推进了一个集成环境控制、消耗品管理、警报和调度的系统。此外，通过使用摄像机视觉估计产量和使用机器学习预测产量和优化环境变量，不断改进自动控制。为了使该系统具有成本效益，将开发一种新型的多光谱相机来收集农业数据。利用机器学习，收集的图像将与环境变量相关，如pH和营养浓度，因此这些变量可以进行优化，以提高产量。为了改善调度和物流，该平台将跟踪种子、营养物质和pH溶液等输入，使用传感器和二维码来自动更换消耗品。传感器和软件检查将在组件或人为故障导致作物歉收之前确定何时发生组件或人为故障，从而及时更换和维护组件。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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve vertical farms. The global threat to food security and the need to deal with unpredictable climate conditions have opened the doors to advanced precision agriculture and vertical urban farming. Rapidly growing global populations, demand for higher agricultural yields with limited arable farmland drive demand for vertical farming and new technologies to expand access to this method of sustainable crop cultivation. The proposed system will reduce the burden on the environment by decreasing water, fertilizers and pesticides required to grow crops. These micro-farms will boost agricultural profitability by providing a reliable, sustainable food system with fresh, healthy, and eco-friendly produce. This reduces food waste and the environmental impact of the food supply chain while improving yields and nutritional density.This SBIR Phase II project advances a system with integrated environmental control, consumables management, alerting, and scheduling. Furthermore, the automated controls are continuously improved by using camera vision to estimate yield and machine learning to forecast yield and optimize the environmental variables. To make the system cost-effective, a novel multispectral camera will be developed to collect agricultural data. Using machine learning, the images collected will be correlated to the environmental variables, such as pH and nutrient concentration, so that those variables can be optimized to increase yields. In order to improve scheduling and logistics, the platform will track inputs such as seeds, nutrients, and pH solution, using sensors and QR codes to automate consumable replacement. Sensors and software checks will determine when component or human failure have occurred, before they lead to crop failure, leading to just-in-time component replacement and maintenance.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.