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A Machine-Learning Approach for Automatic Grape-Bunch Detection Based on Opponent Colors

Author

Listed:
  • Vittoria Bruni

    (Department of Basic and Applied Sciences for Engineering, Sapienza Rome University, Via Antonio Scarpa 16, 00161 Rome, Italy
    These authors contributed equally to this work.)

  • Giulia Dominijanni

    (Department of Basic and Applied Sciences for Engineering, Sapienza Rome University, Via Antonio Scarpa 16, 00161 Rome, Italy
    These authors contributed equally to this work.)

  • Domenico Vitulano

    (Department of Basic and Applied Sciences for Engineering, Sapienza Rome University, Via Antonio Scarpa 16, 00161 Rome, Italy
    These authors contributed equally to this work.)

Abstract

This paper presents a novel and automatic artificial-intelligence (AI) method for grape-bunch detection from RGB images. It mainly consists of a cascade of support vector machine (SVM)-based classifiers that rely on visual contrast-based features that, in turn, are defined according to grape bunch color visual perception. Due to some principles of opponent color theory and proper visual contrast measures, a precise estimate of grape bunches is achieved. Extensive experimental results show that the proposed method is able to accurately segment grapes even in uncontrolled acquisition conditions and with limited computational load. Finally, such an approach requires a very small number of training samples, making it appropriate for onsite and real-time applications that are implementable on smart devices, usable and even set up by winemakers.

Suggested Citation

  • Vittoria Bruni & Giulia Dominijanni & Domenico Vitulano, 2023. "A Machine-Learning Approach for Automatic Grape-Bunch Detection Based on Opponent Colors," Sustainability, MDPI, vol. 15(5), pages 1-24, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4341-:d:1083776
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