Towards Intelligent Pruning of Vineyards by Direct Detection of Cutting Areas

Elia Pacioni, Eugenio Abengózar, Miguel Macías Macías (), Carlos J. García-Orellana, Ramón Gallardo and Horacio M. González Velasco
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Elia Pacioni: Centro Universitario de Mérida, Universidad de Extremadura, Avda. Santa Teresa de Jornet, 38, 06800 Mérida, Spain
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
Carlos J. García-Orellana: Facultad de Ciencias, Universidad de Extremadura, Avda. de Elvas, s/n, 06006 Badajoz, Spain
Ramón Gallardo: Instituto de Computación Científica Avanzada, Av. de la Investigación, s/n, 06006 Badajoz, Spain
Horacio M. González Velasco: Instituto de Computación Científica Avanzada, Av. de la Investigación, s/n, 06006 Badajoz, Spain

Agriculture, 2025, vol. 15, issue 11, 1-15

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.

Keywords: computer vision; object segmentation; Mask R-CNN; YOLOv8; vineyard pruning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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