SBIR Phase II: Apple Yield Mapping using Computer Vision

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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.