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Towards Intelligent Pruning of Vineyards by Direct Detection of Cutting Areas

Author

Listed:
  • Elia Pacioni

    (Centro Universitario de Mérida, Universidad de Extremadura, Avda. Santa Teresa de Jornet, 38, 06800 Mérida, Spain
    HES-SO Valais/Wallis, 3960 Sierre, Switzerland)

  • Eugenio Abengózar

    (Facultad de Ciencias, Universidad de Extremadura, Avda. de Elvas, s/n, 06006 Badajoz, Spain)

  • Miguel Macías Macías

    (Centro Universitario de Mérida, Universidad de Extremadura, Avda. Santa Teresa de Jornet, 38, 06800 Mérida, Spain
    Instituto de Computación Científica Avanzada, Av. de la Investigación, s/n, 06006 Badajoz, Spain)

  • Carlos J. García-Orellana

    (Facultad de Ciencias, Universidad de Extremadura, Avda. de Elvas, s/n, 06006 Badajoz, Spain
    Instituto de Computación Científica Avanzada, Av. de la Investigación, s/n, 06006 Badajoz, Spain)

  • Ramón Gallardo

    (Instituto de Computación Científica Avanzada, Av. de la Investigación, s/n, 06006 Badajoz, Spain
    Escuela Politécnica, Universidad de Extremadura, Avda. Universidad s/n, 10003 Cáceres, Spain)

  • Horacio M. González Velasco

    (Instituto de Computación Científica Avanzada, Av. de la Investigación, s/n, 06006 Badajoz, Spain
    Escuela Politécnica, Universidad de Extremadura, Avda. Universidad s/n, 10003 Cáceres, Spain)

Abstract

The development of robots for automatic pruning of vineyards using deep learning techniques seems feasible in the medium term. In this context, it is essential to propose and study solutions that can be deployed on portable hardware, with artificial intelligence capabilities but reduced computing power. In this paper, we propose a novel approach to vineyard pruning by direct detection of cutting areas in real time by comparing Mask R-CNN and YOLOv8 performances. The studied object segmentation architectures are able to segment the image by locating the trunk, and pruned and not pruned vine shoots. Our study analyzes the performance of both frameworks in terms of segmentation efficiency and inference times on a Jetson AGX Orin GPU. To compare segmentation efficiency, we used the mAP50 and AP50 per category metrics. Our results show that YOLOv8 is superior both in segmentation efficiency and inference time. Specifically, YOLOv8-S exhibits the best tradeoff between efficiency and inference time, showing an mAP50 of 0.883 and an AP50 of 0.748 for the shoot class, with an inference time of around 55 ms on a Jetson AGX Orin.

Suggested Citation

  • Elia Pacioni & Eugenio Abengózar & Miguel Macías Macías & Carlos J. García-Orellana & Ramón Gallardo & Horacio M. González Velasco, 2025. "Towards Intelligent Pruning of Vineyards by Direct Detection of Cutting Areas," Agriculture, MDPI, vol. 15(11), pages 1-15, May.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:11:p:1154-:d:1665850
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