I-Corps: Computer Vision based Pre-Harvest Yield Mapping

I-Corps：基于计算机视觉的收获前产量绘图

• 批准号: 1745209
• 负责人: Ibrahim Isler
• 金额: $ 5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745209&HistoricalAwards=false
• 关键词: Corps; Computer; Vision; based; Pre

The broader impact/commercial potential of this I-Corps project is the practical deployment of a computer-vision based yield estimation system in fruit orchards. With this technology, the farmers will be able to obtain a map of the number and size of fruit across their farms from images recorded by a standard camera. This capability will provide farmers with a useful tool in planning their harvest and sales, as well as in managing the long-term health of their plants. It will also potentially reduce the inherent risk in fruit growing, thereby improving the affordability and availability of fresh fruit in the US. Furthermore, by allowing the adoption of precision agriculture techniques to high value crops, this project may help save water and contribute to reduction of runoff pollution from fertilizer and chemicals.This I-Corps project addresses the task of customer discovery for applications of the technology of vision-based yield detection and mapping in fruit orchards. The core of the technology is a set of computer vision algorithms to detect fruit in images under natural farm conditions. This application presents several computer vision task challenges. Fruit may be occluded by leaves and other fruit. Shadows and specularities make color information unreliable. Also, fruit across frames are matched to avoid double counting. Geometric algorithms are used for estimating fruit diameter. This I-Corps project will allow for customer discovery in this and other market spaces, test the product-market fit for the proposed yield estimation technology, and guide future technology development in this area.

I-Corps项目的更广泛影响/商业潜力是在果园实际部署基于计算机视觉的产量估算系统。有了这项技术，农民将能够从标准相机记录的图像中获得整个农场水果数量和大小的地图。这种能力将为农民提供一种有用的工具，用于规划其收获和销售，以及管理其植物的长期健康。它还将潜在地降低水果种植的内在风险，从而提高美国新鲜水果的可负担性和可获得性。此外，通过允许在高价值作物上采用精准农业技术，该项目可能有助于节约用水，并有助于减少化肥和化学品的径流污染。这个I-Corps项目解决了在果园中应用基于视觉的产量检测和绘图技术的客户发现任务。该技术的核心是一套计算机视觉算法，用于在自然农场条件下检测图像中的水果。该应用程序提出了几个计算机视觉任务挑战。果实可能被叶子和其他果实遮挡。阴影和高光使颜色信息不可靠。此外，跨帧的水果是匹配的，以避免重复计算。用几何算法估计果实直径。这个I-Corps项目将允许在这个和其他市场空间中发现客户，测试产品市场是否适合拟议的产量估算技术，并指导该领域的未来技术发展。