Vineyard pruning weight assessment by machine vision: towards an on-the-go measurement system

Borja Millan,Maria Paz Diago,Javier Tardaguila,Arturo Aquino,Fernando Palacios

doi:10.20870/oeno-one.2019.53.2.2416

Borja Millan, Maria Paz Diago + Show 3 more

Open Access

https://doi.org/10.20870/oeno-one.2019.53.2.2416

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Abstract

Aim: Pruning weight is an indicator of vegetative growth and vigour in grapevine. Traditionally, it is manually determined, which is time-consuming and labour-demanding. This study aims at providing a new, non-invasive and low-cost method for pruning weight estimation in commercial vineyards based on computer vision.Methods and results: The methodology relies on computer-based analysis of RGB images captured manually and on-the-go in a VSP Tempranillo vineyard. Firstly, the pruning weight estimation was evaluated using manually taken photographs using a controlled background. These images were analysed to generate a model of wood pruning weight estimation, resulting in a coefficient of determination (R2) of 0.91 (p<0.001) and a root-mean-square error (RMSE) of 87.7 g. After this, a mobile sensor platform (modified ATV) was used to take vine images automatically and on-the-go without background. These RGB images were analysed using a fully automated computer vision algorithm, resulting in R2 = 0.75 (p<0.001) and RMSE = 147.9 g. Finally, the mobile sensor platform was also used to sample a commercial VSP vineyard to map the spatial variability of wood pruning weight, and hereafter vine vigour.Conclusions: The results showed that the developed computer vision methodology was able to estimate the vine pruning weight in commercial vineyards and to map the spatial variation of the pruning weight across a vineyard.Significance and impact of the study: The presented methodology may become a valuable tool for the wine industry for rapid assessment and mapping of vine vigour. This information can be used to support decision making on pruning, fertilization and canopy management.

Highlights

Pruning weight is an important indicator used to appraise biomass production, carbon storage cycle, vigour and vine balance (Smart and Robinson, 1991; Keller, 2015)
The model was externally validated using leave-one-out cross-validation (LOOCV), which resulted in R2 = 0.91, root-mean-square error (RMSE) = 87.7 g and mean absolute error (MAE) = 61.7 g
Our results show that the new computer visionbased method, using a simple RGB camera, is rapid and reliable for grapevine pruning weight assessment under field conditions with performance values similar to those reported in previous studies using more sophisticated sensors installed in ground-moving vehicles

Summary

Introduction

Pruning weight is an important indicator used to appraise biomass production, carbon storage cycle, vigour and vine balance (Smart and Robinson, 1991; Keller, 2015). Airborne sensors can be used to monitor entire vineyards but at the cost of expensive technologies, weather constraints and costly revisiting operations. Another limitation of the utilisation of aerial sensors to monitor non-continuous crops (like vineyards) from zenithal view is the influence of soil reflectance in the calculation of indices (Stamatiadis et al, 2006). Using portable sensors for manually vineyard monitoring is slow and labour demanding To overcome this pitfall, on-the-go sensors have been recently used for vineyard monitoring (Sepúlveda-Reyes et al, 2016; Palleja and Landers, 2017; Fernández-Novales et al, 2018), reducing the cost, especially when conducted simultaneously to another vineyard operation, like tillage

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