SBIR-STTR Award

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.

The broader impact/commercial potential of this project is to use the proposed system for automation of yield mapping so that growers will be able to improve their growing and harvesting processes. Yield mapping is critical for fruit growers. An accurate estimate is enormously beneficial to sales operations, harvest time logistics, and crop management. Currently, yield mapping is performed manually in a difficult, laborious process prone to sampling and counting error. The proposed system would enable growers to sell better fruit at higher prices while using less resources. By bringing improved certainty to harvest quantity and timing, the system will also improve the efficiency of the entire fruit supply chain, making fresh fruit more readily available in stores at more consistent prices. This Small Business Innovation Research (SBIR) Phase II project will address the problem of automated yield mapping for fruit crops. Rather than relying on expensive sensing equipment such as laser-based lidar scanners, the company proposes to build a robust, yet inexpensive, fruit mapping system using commercial, off-the-shelf components. In order to achieve this goal, significant computer vision and systems challenges must be overcome. These include: (1) Adapting and developing accurate computer vision algorithms for fruit detection and segmentation, as well as geometric algorithms for sizing and tracking fruit and mapping the foliage; and (2) Building usable and reliable systems for scanning, upload, cloud processing, and results visualization. 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.