Abstract
Automated fruit counting, through the use of computer vision methods, is a very important step towards the development of precision agriculture, and, as a consequence, has been object of study by multiple authors. In this work, we present a novel method to detect, track and count fruits that is scalable and robust towards problems existing in other fruit counting methods. Namely, our approach consists in a method that uses (1) a stereo camera that can be used reliably under direct sunlight, (2) a fast detection algorithm, and (3) an algorithmic approach to track fruits which is robust to fruit occlusions. We applied our proposed solution in an apple orchard and were able to provide apple counts with an error ranging from 15% to 57%. The characteristics of this novel approach and the preliminary results achieved seem promising in order to tackle the problem of fruit counting with occlusions at the large scale of a whole orchard, since it addresses the problem of intermittent occlusions that is overlooked by other approaches. Even with errors, the overview of the entire orchard, resulting from the scalability of the process, which can be implemented with a camera in a tractor, makes real-time mapping possible, supporting technical decisions with major economical impact, such as supplementary fruit thinning, water or nutrient adjustments or harvest management.
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SISCOG - Sistemas Cognitivos, SA. https://www.siscog.pt/en-gb/rd/.
References
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Gené-Mola, J., et al.: Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry. Comput. Electron. Agric. 169, 105165 (2020). https://doi.org/10.1016/j.compag.2019.105165
Gongal, A., Amatya, S., Karkee, M., Zhang, Q., Lewis, K.: Sensors and systems for fruit detection and localization: a review. Comput. Electron. Agric. 116, 8–19 (2015). https://doi.org/10.1016/j.compag.2015.05.021
Gongal, A., Silwal, A., Amatya, S., Karkee, M., Zhang, Q., Lewis, K.: Apple crop-load estimation with over-the-row machine vision system. Comput. Electron. Agric. 120, 26–35 (2016).https://doi.org/10.1016/j.compag.2015.10.022
INIAV: Instituto nacional de investigação agrária e veterinária. https://www.iniav.pt/. Accessed 24 Mar 2023
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, X., et al.: Monocular camera based fruit counting and mapping with semantic data association. IEEE Robot. Autom. Lett. 4(3), 2296–2303 (2019). https://doi.org/10.1109/LRA.2019.2901987
Matos, G.P., Santiago, C., Costeira, J.P., Saldanha, R.L., Morgado, E.M.: Tracking and counting apples in orchards under intermittent occlusions and low frame rates. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5413–5421 (2024)
Nguyen, T.T., Vandevoorde, K., Wouters, N., Kayacan, E., De Baerdemaeker, J.G., Saeys, W.: Detection of red and bicoloured apples on tree with an RGB-D camera. Biosyst. Eng. 146, 33–44 (2016). https://doi.org/10.1016/j.biosystemseng.2016.01.007
Redmon, J.: DarkNet: Open Source Neural Networks in C (2013–2016). http://pjreddie.com/darknet/
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Skalski, P.: Make Sense (2019). https://github.com/SkalskiP/make-sense/
Stein, M., Bargoti, S., Underwood, J.: Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors 16(11), 1915 (2016). https://doi.org/10.3390/s16111915
Tao, Y., Zhou, J.: Automatic apple recognition based on the fusion of color and 3D feature for robotic fruit picking. Comput. Electron. Agric. 142, 388–396 (2017). https://doi.org/10.1016/j.compag.2017.09.019
Ullman, S.: The interpretation of structure from motion. Proc. Royal Soc. Lond. Ser. B Biol. Sci. 203(1153), 405–426 (1979)
Wang, Q., Nuske, S., Bergerman, M., Singh, S.: Automated crop yield estimation for apple orchards. In: Desai, J.P., Dudek, G., Khatib, O., Kumar, V. (eds.) Experimental Robotics, vol. 88, pp. 745–758. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-319-00065-7_50
Acknowledgements
To INIAV [5], for providing access to the orchards, for collecting images and data. To the colleagues André Leitão, Carolina Resende and Sara Barros from SISCOG, for the help with ground truth annotations. This work was supported by LARSyS funding (DOI: 10.54499/LA/P/0083/2020, 10.54499/UIDP/50009/2020, and 10.54499/UIDB/50009/2020) and 10.54499/2022.07849.CEECIND/CP1713/CT0001, through Fundação para a Ciência e a Tecnologia, and by the SmartRetail project [PRR - C645440011-00000062], through IAPMEI - Agência para a Competitividade e Inovação.
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Matos, G.P. et al. (2025). An Apple Counting System Robust to Multiple Intermittent Occlusions. In: Santos, M.F., Machado, J., Novais, P., Cortez, P., Moreira, P.M. (eds) Progress in Artificial Intelligence. EPIA 2024. Lecture Notes in Computer Science(), vol 14967. Springer, Cham. https://doi.org/10.1007/978-3-031-73497-7_15
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