Abstract
Vision-based grape yield estimation provides a cost-effective solution for intelligent orchards. However, unstructured background, occlusion and dense berries make it challenging for grape yield estimation. We propose an efficient two-stage pipeline TSGYE: precise detection of grape clusters and efficient counting of grape berries. Firstly, high-precision grape clusters are detected using object detectors, such as Mask R-CNN, YOLOv2/v3/v4. Secondly, based on the detected clusters, berry counted through image processing technology. Experimental results show that TSGYE with YOLOv4 achieves 96.96% mAP@0.5 score on WGISD, better than the state-of-the-art detectors. Besides we manually annotate all test images of WGISD and make it public with a grape berry counting benchmark. Our work is a milestone in grape yield estimation for two reasons: we propose an efficient two-stage grape yield estimation pipeline TSGYE; we offer a public test set in grape berry counting for the first time.
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References
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under grant No. 61972268 and No. 61906126, and is also supported by the project of Technology Innovation and Development of Chengdu Science and Technology Bureau (No.2019-YF05-01126-SN).
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Deng, G., Geng, T., He, C., Wang, X., He, B., Duan, L. (2020). TSGYE: Two-Stage Grape Yield Estimation. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_66
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