SBIR Phase I: Improving indoor agriculture grow light efficiency with adaptive light shaping

SBIR 第一阶段：通过自适应光整形提高室内农业种植光效率

• 批准号: 2304339
• 负责人: Andrew Rape
• 金额: $ 27.45万
• 依托单位: LEAFICIENT INC
• 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: 2023-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2304339&HistoricalAwards=false
• 关键词: SBIR; Phase; Improving; indoor; agriculture

The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is in accelerating the transition to a more sustainable food supply chain, which could democratize access to healthy foods and lessen food insecurity. The US system for growing and distributing fresh food is inefficient and insufficient in the context of climate change. Controlled environmental agriculture (CEA) is seen as a potentially revolutionary way to supply food demands with limited resources. Despite this promise, the high energy requirements of creating lighting and cooling in enclosed environments to support photosynthesis has caused CEA not to realize its promise. In the project, a novel solution to improve energy efficiency in CEA farms is proposed where advanced optics, machine learning, and computer vision are used to ensure that all of the light that is emitted by synthetic light sources is optimally used for plant photosynthesis and growth. The project offers a plausible way to create reliably profitable operations for CEA producers which would lead to enhanced access to fresh produce for consumers and decreased reliance on conventional agriculture to meet the world’s food needs.Within current commercial grow systems for controlled environment agriculture, a substantial portion of the photons are wasted as they are not incident onto photosynthetically active biomass and are absorbed by the surrounding grow rack and media. This project will prototype and systematically test a light production system that dynamically shapes light such that it is rendered only onto the photosynthetic areas of the plant. To accomplish this, the project will develop and evaluate (within three crop varieties) a closed-loop system to autonomously detect the three-dimensional shape of the growing plant and dynamically adjust the light intensity and projection area to optimize power efficiency and biomass growth. Successful completion of the work in this project will result in a novel technology that is systematically tested to yield similar quality produce using a fraction of the energy consumption of current state-of-the-art systems. Deployment of this technology would help to improve the unit economics of controlled environment agriculture produce items and accelerate adoption of controlled environment agriculture farming practices that potentially consume less water, utilize land resources more efficiently, and eliminate the need for chemical pesticide/herbicide treatments.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.

这一小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是加速向更可持续的食品供应链过渡，这可以使获得健康食品的机会民主化，并减少粮食不安全。 在气候变化的背景下，美国种植和分配新鲜食品的系统效率低下，而且不足。受控环境农业（CEA）被认为是一种潜在的革命性方式，以有限的资源满足粮食需求。 尽管有这样的承诺，但在封闭环境中创造照明和冷却以支持光合作用的高能量需求导致CEA无法实现其承诺。 在该项目中，提出了一种提高CEA农场能源效率的新解决方案，其中使用先进的光学、机器学习和计算机视觉来确保合成光源发出的所有光都能最佳地用于植物光合作用和生长。 该项目为CEA生产商提供了一种可行的方式来创造可靠的盈利业务，这将导致消费者更容易获得新鲜农产品，并减少对传统农业的依赖，以满足世界的粮食需求。相当大部分的光子被浪费，因为它们没有入射到光合活性生物质上，机架和介质。该项目将原型化并系统地测试一个光产生系统，该系统动态地塑造光，使其仅呈现在植物的光合区域上。 为了实现这一目标，该项目将开发和评估（在三种作物品种内）一个闭环系统，以自主检测生长植物的三维形状，并动态调整光照强度和投影面积，以优化功率效率和生物量增长。该项目工作的成功完成将产生一种新技术，该技术经过系统测试，使用当前最先进系统的一小部分能耗生产类似质量的产品。 这项技术的部署将有助于提高受控环境农业生产项目的单位经济效益，并加速采用受控环境农业耕作方法，这些方法可能消耗更少的水，更有效地利用土地资源，消除对化学杀虫剂的需求/该奖项反映了NSF的法定使命，并通过使用基金会的知识产权进行评估，优点和更广泛的影响审查标准。