SBIR Phase I: Apple Yield Mapping using Computer Vision

该项目更广泛的影响/商业潜力是在果园中实际部署基于计算机视觉的产量估计系统。 这将为果农提供一个有用的工具，以规划他们的收获和销售，以及管理他们的植物的长期健康。 这将潜在地降低水果种植的固有风险，从而提高美国新鲜水果的可负担性和可获得性。 更好的确定性可能特别有助于小种植者，因为他们管理风险的现有能力较低。 此外，通过将精准农业技术应用于高价值作物，该项目可以帮助节约用水，并有助于减少化肥和化学品的径流污染。这个小型企业创新研究（SBIR）第一期项目解决了基于视觉的水果检测和水果映射问题，以实现产量映射。 研究目的是提高现有产量制图方法的商业可用性和鲁棒性，使其能够应用于生产型果园。 该项目的预期技术成果包括在各种真实的世界生产果园环境中，在各种天气和光照条件下，展示苹果的有用产量图。

The broader impact/commercial potential of this project is the practical deployment of a computer-vision based yield estimation system in fruit orchards. This will provide fruit farmers with a useful tool in planning their harvest and sales, as well as in managing the long-term health of their plants. This will potentially reduce the inherent risk in fruit growing, thereby improving the affordability and availability of fresh fruit in the US. Better certainty may particularly help small growers, because they have less existing capability to manage risk. Furthermore, by allowing adoption of precision agriculture techniques techniques to high value crops, this project may help save water and contribute to reduction of runoff pollution from fertilizer and chemicals.This Small Business Innovation Research (SBIR) Phase I project addresses the problem of vision-based fruit detection and mapping of fruit, for purposes of yield mapping. The research objectives are to improve the commercial usability and robustness of existing yield-mapping approaches, so that they can be applied in production fruit orchards. The anticipated technical results of this project include demonstration of useful yield mapping of apples, in a variety of real world production orchard environments, in a variety of weather and lighting conditions.