3D Assessment of Vine Training Systems Derived from Ground-Based RGB-D Imagery

Hugo Moreno, José Bengochea-Guevara, Angela Ribeiro and Dionisio Andújar
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Hugo Moreno: Centre for Automation and Robotics, CSIC–UPM (Consejo Superior de Investigaciones Científicas–Universidad Politécnica de Madrid), Arganda del Rey, 28500 Madrid, Spain
José Bengochea-Guevara: Centre for Automation and Robotics, CSIC–UPM (Consejo Superior de Investigaciones Científicas–Universidad Politécnica de Madrid), Arganda del Rey, 28500 Madrid, Spain
Angela Ribeiro: Centre for Automation and Robotics, CSIC–UPM (Consejo Superior de Investigaciones Científicas–Universidad Politécnica de Madrid), Arganda del Rey, 28500 Madrid, Spain
Dionisio Andújar: Centre for Automation and Robotics, CSIC–UPM (Consejo Superior de Investigaciones Científicas–Universidad Politécnica de Madrid), Arganda del Rey, 28500 Madrid, Spain

Agriculture, 2022, vol. 12, issue 6, 1-18

Abstract: In the field of computer vision, 3D reconstruction of crops plays a crucially important role in agriculture. On-ground assessment of geometrical features of vineyards is of vital importance to generate valuable information that enables producers to take the optimum actions in terms of agricultural management. A training system of vines ( Vitis vinifera L.), which involves pruning and a trellis system, results in a particular vine architecture, which is vital throughout the phenological stages. Pruning is required to maintain the vine’s health and to keep its productivity under control. The creation of 3D models of vineshoots is of crucial importance for management planning. Volume and structural information can improve pruning systems, which can increase crop yield and improve crop management. In this experiment, an RGB-D camera system, namely Kinect v2, was used to reconstruct 3D vine models, which were used to determine shoot volume on eight differentiated vineyard training systems: Lyre, GDC (Geneva Double Curtain), Y-Trellis, Pergola, Single Curtain, Smart Dyson, VSP (Vertical Shoot Positioned), and the head-trained Gobelet. The results were compared with dry biomass ground truth-values. Dense point clouds had a substantial impact on the connection between the actual biomass measurements in four of the training systems (Pergola, Curtain, Smart Dyson and VSP). For the comparison of actual dry biomass and RGB-D volume and its associated 3D points, strong linear fits were obtained. Significant coefficients of determination (R 2 = 0.72 to R 2 = 0.88) were observed according to the number of points connected to each training system separately, and the results revealed good correlations with actual biomass and volume values. When comparing RGB-D volume to weight, Pearson’s correlation coefficient increased to 0.92. The results reveal that the RGB-D approach is also suitable for shoot reconstruction. The research proved how an inexpensive optical sensor can be employed for rapid and reproducible 3D reconstruction of vine vegetation that can improve cultural practices such as pruning, canopy management and harvest.

Keywords: depth cameras; Kinect v2; 3D reconstruction; woody crops; Vitis vinifera L.; vineyards; vine training systems (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: 2022
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