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A perception-guided CNN for grape bunch detection

Author

Listed:
  • Bruni, Vittoria
  • Dominijanni, Giulia
  • Vitulano, Domenico
  • Ramella, Giuliana

Abstract

Precision Viticulture (PV) is becoming an active and interdisciplinary research field since it requires solving interesting research issues to concretely answer the demands of specific use cases. A challenging problem in this context is the development of automatic methods for yield estimation. Computer vision methods can contribute to the accomplishment of this task, especially those that can replicate what winemakers do manually. In this paper, an automatic artificial intelligence method for grape bunch detection from RGB images is presented. A customized Convolutional Neural Network (CNN) is employed for pointwise classification of image pixels and the dependence of classification results on the type of input color channels and grapes color properties are studied. The advantage of using additional perception-based input features, such as luminance and visual contrast, is also evaluated, as well as the dependence of the method on the choice of the training set in terms of the amount of labeled data. The latter point has a significant impact on the practical use of the method on-site, its usability by non-expert users, and its adaptability to individual vineyards. Experimental results show that a properly trained CNN can discriminate and detect grape bunches even under uncontrolled acquisition conditions and with limited computational load, making the proposed method implementable on smart devices and suitable for on-site and real-time applications.

Suggested Citation

  • Bruni, Vittoria & Dominijanni, Giulia & Vitulano, Domenico & Ramella, Giuliana, 2025. "A perception-guided CNN for grape bunch detection," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 230(C), pages 111-130.
    • Handle: RePEc:eee:matcom:v:230:y:2025:i:c:p:111-130
    DOI: 10.1016/j.matcom.2024.11.004
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    References listed on IDEAS

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    1. 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.
    2. Yun Peng & Aichen Wang & Jizhan Liu & Muhammad Faheem, 2021. "A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    3. Bruni, V. & Rossi, E. & Vitulano, D., 2014. "Automated restoration of semi-transparent degradation via Lie groups and visibility laws," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 106(C), pages 109-123.
    4. Xiang Huang & Dongdong Peng & Hengnian Qi & Lei Zhou & Chu Zhang, 2024. "Detection and Instance Segmentation of Grape Clusters in Orchard Environments Using an Improved Mask R-CNN Model," Agriculture, MDPI, vol. 14(6), pages 1-21, June.
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