I-Corps: Smartphone-based peanut maturity grading and analytic system

这个i-Corps项目的更广泛的影响/商业潜力将是通过提供准确和一致的基于颜色的花生分类来提高作物产量和质量。许多作物是根据颜色分类来确定收获日期和质量的。此外，优化收获期是最大限度提高花生质量和产量的关键。总产量的损失可能是由于低估或高估了收获日期而造成的。此外，高估可能会导致销售价格较低，花生品质因不成熟而不佳，而低估可能会导致过熟的作物因茎弱而留在土壤中。目前，花生的颜色分类过程是基于人类视觉的。与它们相比，该技术将提供标准化、准确和一致的花生分级结果。此外，积累的数据将为金融机构、食品行业和种植者提供有关花生定价、产量和质量的最新见解。这个i-Corps项目将为团队提供探索技术商业化的机会。这个i-Corps项目是第一个花生成熟度预测和分析系统，旨在简化和简化当前的程序。现有的成熟度预测方法是基于人类视觉的，由于测试的主观性质，这可能会导致结果的可变性，完成一组200多个花生样本需要30多分钟。在该方案中，开发了一个基于颜色分类的标准化花生成熟度预测和分析系统。该系统包括一个光控照相亭、一个花生分拣板和一部智能手机，每次可以对多达400个花生进行分级和分析。自动化将使评分过程在3分钟内完成，并为用户生成报告。这将有助于在每个花生季节为每个花生分级机节省500-800小时的人工成本。此外，还将开发花生信息系统，通过分级程序收集花生信息。这些信息可用于提供花生市场的定价和生长预测信息。已经开发了几个原型来证明这一概念。已经对花生样本进行了测试，以证明拟议技术的概念。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 I-Corps project will be the improvement of crop yield and quality by providing accurate and consistent color-based peanut classification. Many crops are based on color classification to determine the harvest day and quality. Also, optimizing harvest dates is a key to maximizing peanut quality and production. A loss of the total yield can be caused by underestimating or overestimating the harvest dates. In addition, overestimation can cause a low selling price and suboptimal peanut quality due to immaturity, while underestimation may cause over-mature crops to be left in the soil due to the weak stems. Currently, the color classification procedures in peanuts are human-vision based. Comparing to them, this technology will provide a standardized, accurate and consistent peanut grading results. Also, the accumulated data will provide updated insights to financial institutes, food industries, growers on peanut pricing, yield and quality. This I-Corps program will provide opportunities for the team to explore commercialization of the technology.This I-Corps project is the first peanut maturity prediction and analytic system designed to streamline and simplify the current procedure. Existing method for maturity prediction is based on human vision, which can lead to variability in results due to the subjective nature of the test, and 30+ minutes to finish one set of 200+ peanut samples. In this proposal, a standardized peanut maturity prediction and analytic system based on color classification has been developed. The system includes a light-controlled photo booth, a peanut sorting board and a smartphone to grade and analyze up to 400 peanuts each time. Automation will allow the grading process to be completed in under 3 minutes and generate a report for the user. This will help save 500-800 hours of labor cost per peanut grader in each peanut season. Furthermore, a peanut information system will be developed to collect peanut information through the grading procedure. This information can be used for providing pricing and growth prediction information in the peanut market. Several prototypes have been developed to prove the concept. Tests have been performed with peanut samples to prove the concept of the proposed technology.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.