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Leveraging Computer Vision for Precision Viticulture

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Computer Vision and Machine Learning in Agriculture, Volume 3

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Economic importance of wine industry globally supports the development of innovative computer vision algorithms towards precision viticulture, aiming to maximize grapes’ quantity and quality while minimizing input costs. Computer vision has the potential to provide inexpensive and non-destructive means to capture and extract precise information about the vineyard. A set of typical viticulture practices have already benefitted from the technical advances in computer vision. This work aims to present a comprehensive review of computer vision applications in precision viticulture. The research focuses on the typical vineyard management calendar, providing frameworks to work activities in the vineyard, by months of the year, based on the annual grapevine growth cycle. Therefore, all typical annual viticulture practices are examined for the first time holistically, revealing the gaps in their automation, posing new challenges and objectives that have not yet been explored. This work intends to deliver a complete guide of the current status of computer vision in viticulture, covering all management practices, such as pruning, binding, shoot thinning, weeding, spraying, leaf thinning, top**, cluster thinning, harvesting, and more. The limitations of current computer vision techniques are analysed, and future potentials are discussed.

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Authors and Affiliations

  1. MLV Research Group, Department of Computer Science, International Hellenic University, 65404, Kavala, Greece

    Eleni Vrochidou & George A. Papakostas

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  1. Eleni Vrochidou
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  2. George A. Papakostas
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Correspondence to Eleni Vrochidou .

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  1. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  2. Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh

    Mohammad Shorif Uddin

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Vrochidou, E., Papakostas, G.A. (2023). Leveraging Computer Vision for Precision Viticulture. In: Bansal, J.C., Uddin, M.S. (eds) Computer Vision and Machine Learning in Agriculture, Volume 3. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3754-7_13

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