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Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-Up Images

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Advances in Neural Networks – ISNN 2020 (ISNN 2020)

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Abstract

A machine vision system for apple harvesting robot was developed based on the YOLOv3 and the YOLOv5 algorithms with special pre- and post-processing and the YOLOv3 equipped with special pre- and post-processing procedures is able to achieve an a share of undetected apples (FNR) at 9.2% in the whole set of images, 6,7% in general images, and 16,3% in close-up images. A share of objects mistaken for apples (FPR) was at 7.8%. The YOLOv5 can detect apples quite precisely without any additional techniques, showing FNR at 2.8% and FPR at 3.5%.

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Authors and Affiliations

  1. Financial University Under the Government of the Russian Federation, 38 Shcherbakovskaya, Moscow, 105187, Russia

    Anna Kuznetsova, Tatiana Maleva & Vladimir Soloviev

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  1. Anna Kuznetsova
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  2. Tatiana Maleva
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  3. Vladimir Soloviev
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Correspondence to Vladimir Soloviev .

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Editors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China

    Min Han

  2. School of Science, Harbin Institute of Technology, Weihai, China

    Sitian Qin

  3. School of Engineering and Applied Sciences, University of the District of Columbia, Washington, DC, USA

    Nian Zhang

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Kuznetsova, A., Maleva, T., Soloviev, V. (2020). Detecting Apples in Orchards Using YOLOv3 and YOLOv5 in General and Close-Up Images. In: Han, M., Qin, S., Zhang, N. (eds) Advances in Neural Networks – ISNN 2020. ISNN 2020. Lecture Notes in Computer Science(), vol 12557. Springer, Cham. https://doi.org/10.1007/978-3-030-64221-1_20

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  • DOI: https://doi.org/10.1007/978-3-030-64221-1_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64220-4

  • Online ISBN: 978-3-030-64221-1

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