On‐the‐go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis
María P. Diago, Arturo Aquino, Borja Millán, Fernando Palacios, Javier Tardáguila
- Year
- 2019
- Citations
- 35
Abstract
Background and Aims Canopy assessment of the fruiting zone can lead to more informed vineyard management decisions. A non-destructive, image-based system capable of operating on-the-go was developed to assess canopy porosity, and leaf and bunch exposure of red grape cultivars in the vineyard. Methods and Results On-the-go (7 km/h) night time images of a vertically shoot positioned commercial vineyard canopy were acquired with an automated red green blue imaging system, coupled to a GPS and controlled artificial lighting. The reference method was point quadrat analysis. Sound correlations between the image analysis and point quadrat analysis results for the proportion of gaps (R2 > 0.85; P < 0.001) and leaf to canopy area ratio (R2 > 0.57; P < 0.001) were obtained for both sides of the canopy. For the bunch to canopy area ratio the best relationship was found on the western side of the canopy (R2 = 0.79; P < 0.001). Also maps of the three canopy variables were built in a commercial vineyard to compare their spatial variability on the east and west sides across the whole vineyard plot. Conclusions The developed imaging system, capable of operating on-the-go, can yield quantitative, objective and reliable knowledge of what a grapegrower would assess by subjective, qualitative visual inspection of the grapevine canopy. The information can be used to help make better informed decisions about leaf removal, and if mapped may help to delineate zones amenable to homogeneous management. Significance of the Study The new developed computer vision system can be mounted on any vehicle, such as a tractor, all terrain vehicle and robot, for a rapid and objective monitoring of the vineyard canopy around the fruiting zone in red cultivars and vertically shoot positioned trained vines. Moreover, the maps generated could be used by a new generation of variable rate viticultural machinery to spatially optimise vineyard cultural practices.
Keywords
Related papers
Artificial intelligence: a modern approach
1995
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
2012
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham +17 more
2016
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller +1 more
2013