Abstract
We present a novel method for yield estimation in apple orchards. Our method takes segmented and registered images of apple clusters as input. It outputs number and location of individual apples in each cluster. Our primary technical contributions are a representation based on a mixture of Gaussians, and a novel selection criterion to choose the number of components in the mixture. The method is experimentally verified on four different datasets using images acquired by a vision platform mounted on an aerial robot, a ground vehicle and a hand-held device. The accuracy of the counting algorithm itself is \(91\%\). It achieves 81–85% accuracy coupled with segmentation and registration which is significantly higher than existing image based methods.
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Acknowledgments
This work is supported in part by NSF grant # 1317788, USDA NIFA MIN-98-G02 and the MnDrive initiative.
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Roy, P., Isler, V. (2017). Vision-Based Apple Counting and Yield Estimation. In: Kulić, D., Nakamura, Y., Khatib, O., Venture, G. (eds) 2016 International Symposium on Experimental Robotics. ISER 2016. Springer Proceedings in Advanced Robotics, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-50115-4_42
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DOI: https://doi.org/10.1007/978-3-319-50115-4_42
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