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

I-Corps项目的更广泛影响/商业潜力将是通过提供准确和一致的花生颜色分类来提高作物产量和质量。许多农作物是根据颜色分类来确定收获日和质量的。此外，优化收获日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收获期造成的。此外，过高估计可能会导致销售价格低，花生因不成熟而品质不佳，而过低估计可能会导致过成熟的作物因茎弱而留在土壤中。目前，花生的颜色分类程序是基于人类视觉的。与之相比，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、种植者提供有关花生价格、产量和质量的最新见解。I-Corps项目将为该团队探索该技术的商业化提供机会。这个I-Corps项目是第一个花生成熟度预测和分析系统，旨在简化和简化目前的程序。现有的成熟度预测方法是基于人的视觉，由于测试的主观性质，可能导致结果的可变性，并且完成一组200+花生样品需要30+分钟。本文提出了一种基于颜色分类的标准化花生成熟度预测与分析系统。该系统包括一个光控照相亭、一个花生分类板和一个智能手机，每次可对多达400颗花生进行分类和分析。自动化将允许评分过程在3分钟内完成，并为用户生成报告。这将有助于每个花生分级机在每个花生季节节省500-800小时的人工成本。此外，还将开发一个花生信息系统，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增长预测信息。已经开发了几个原型来证明这个概念。已经对花生样品进行了测试，以证明拟议技术的概念。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/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.