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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Nellithimaru, A.K., Kantor, G.A.: ROLS: Robust object-level SLAM for grape counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2648–2656 (2019)
Luo, L., Tang, Y., Lu, Q., Chen, X., Zhang, P., Zou, X.: A vision methodology for harvesting robot to detect cutting points on peduncles of double overlapping grape clusters in a vineyard. Comput. Ind. 99, 130–139 (2018)
Nuske, S., Achar, S., Bates, T., Narasimhan, S.G., Singh, S.: Yield estimation in vineyards by visual grape detection. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2352–2358. IEEE (2011)
Barré, P., Herzog, K., Höfle, R., Hullin, M.B., Töpfer, R., Steinhage, V.: Automated phenotyping of epicuticular waxes of grapevine berries using light separation and convolutional neural networks. Comput. Electron. Agric. 156, 263–274 (2019)
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R.J., Fredes, C., Valenzuela, A.: A review of convolutional neural network applied to fruit image processing. Appl. Sci. 10(10), 3443 (2020)
Zabawa, L., Kicherer, A., Klingbeil, L., Töpfer, R., Kuhlmann, H., Roscher, R.: Counting of grapevine berries in images via semantic segmentation using convolutional neural networks. ISPRS J. Photogrammetry Remote Sens. 164, 73–83 (2020)
Santos, T.T., de Souza, L.L., dos Santos, A.A., Avila, S.: Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association. Comput. Electron. Agric. 170, 105247 (2020)
Dunn, G.M., Martin, S.R.: Yield prediction from digital image analysis: a technique with potential for vineyard assessments prior to harvest. Aust. J. Grape Wine Res. 10(3), 196–198 (2004)
Paul Cohen, J., Boucher, G., Glastonbury, C.A., Lo, H.Z., Bengio, Y.: Count-ception: counting by fully convolutional redundant counting. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 18–26 (2017)
Aquino, A., Barrio, I., Diago, M.P., Millan, B., Tardaguila, J.: vitisBerry: an android-smartphone application to early evaluate the number of grapevine berries by means of image analysis. Comput. Electron. Agric. 148, 19–28 (2018)
Nellithimaru, A.K., Kantor, G.A.: ROLS : robust object-level SLAM for grape counting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 2648–2656 (2019)
Bargoti, S., Underwood, J.: Deep fruit detection in orchards. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3626–3633. IEEE (2017)
Goodfelow, I., Bengio, Y., Courville, A.: Deep Learning (Adaptive Computation and Machine Learning Series). MIT Press (2016)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Roscher, R., Herzog, K., Kunkel, A., Kicherer, A., Töpfer, R., Förstner, W.: Automated image analysis framework for the high-throughput determination of grapevine berry sizes using conditional random fields. Comput. Electron. Agri. 100, 148–158 (2017)
Coviello, L., Cristoforetti, M., Jurman, G., Furlanello, C.: In-field grape berries counting for yield estimation using dilated CNNS. arXiv preprint arXiv:1909.12083 (2019)
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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