SBIR Phase I: Novel handheld spectroscopy for the early detection of crop afflictions

该小型企业创新研究（SBIR）第一阶段项目将专注于开发一种手持式光学扫描仪，该扫描仪将精确成像与光谱学相结合。 虽然这项技术有广泛的应用，从工业质量控制到教育，但要解决的滩头阵地是精确农业。为了在2050年养活不断增长的100亿人口，农业生产需要在未来30年内增长70%。这一使命的关键是开发作物保护和健康管理的创新工具和战略，以保护世界粮食供应。这一领域的潜在客户是作物种植者、顾问和田间侦察员，他们的目标是尽早采取行动，同时减少有害和昂贵化学品的使用。2019年全球作物监测市场估计为20亿美元，预计到2027年将达到60亿美元，年增长率为15.3%。传感和成像部分在2019年占市场份额的49.3%。这项技术有可能支持采用更可持续的农业实践，以及经济可行性的中小型农场经营在美国。通过提供一个方便和负担得起的工具，疾病检测和作物健康管理。该项目的智力价值是商业开发的新型和负担得起的成像光谱技术，除了应用-用于早期检测作物病害的特定分析。将在该项目中设计用于制造的扫描设备的光学组件弥合了当前由空间分辨率的光谱分辨率的检测能力的系统所造成的差距。该扫描仪将用于收集葡萄的数据，葡萄是一种重要的高价值作物，容易受到许多疾病的影响，这些疾病会在人眼检测到之前破坏产量。机器学习算法将从这些数据中开发出来，以在工厂的压力和疾病状态变得明显之前进行目视检查。然后，这些模型将根据园艺专家和客户的用户反馈在实地进行验证。这项技术的成功实施将有助于在作物病害蔓延之前发现它们。这种检测能力可以提高产量，同时减少昂贵且对环境有害的农药和化肥的使用。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。

This Small Business Innovation Research (SBIR) Phase I project will focus on development of a handheld optical scanner that integrates precise imaging with optical spectroscopy. Although this technology has broad applications ranging from industrial quality control to education, the beachhead sector to be addressed is precision agriculture. To feed a growing population of 10 billion in 2050, agricultural production will need to increase by 70% over the next 30 years. Critical to this mission is the development of innovative tools and strategies for crop protection and health management to preserve the world’s food supply. The potential customers in this segment are crop growers, consultants, and field scouts aiming to act early while reducing the use of harmful and costly chemicals. The global crop monitoring market was estimated at $2 billion in 2019 and is anticipated to reach $6 billion by 2027, growing at 15.3% annually. The sensing and imaging segment accounted for 49.3% of the market in 2019. This technology has the potential to support the adoption of more sustainable agricultural practices as well as the economic viability of small- to medium-scale farm operations in the U.S. by providing an accessible and affordable tool for disease detection and crop health management.The intellectual merit of this project is the commercial development of novel and affordable imaging spectroscopy technology in addition to application-specific analytics for the early detection of crop afflictions. Optical components of the scanning device that will be designed-for-manufacturing in this project bridge the current gap created by systems that trade the detection capabilities of spectral resolution for spatial resolution. The scanner will be deployed to collect data on grapes, a key, high-value crop that is susceptible to a number of afflictions that destroy yields before the human eye can detect them. Machine learning algorithms will be developed from this data to detect stressed and diseased states in the plant before they become apparent to visual inspection. These models will then be validated in the field with user feedback from horticultural experts and customers. Successful implementation of this technology will facilitate the detection of crop diseases before they spread. This detection capability can increase yields while reducing the use of costly and environmentally-harmful pesticides and fertilizers.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.