On‐the‐go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis

Diago, María P.; Aquino, Arturo; Millán, Borja; Palacios, Fernando Fernández; Tardáguila, Javier

doi:10.1111/ajgw.12404

Cited by 26 publications

(22 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computer vision systems are powerful tools to automate inspection tasks in agriculture [4][5][6][7][8][9][10][11]. Typical target applications of such systems include grading, quality estimation, yield prediction and monitoring, among others [6][7][8]. With computer vision techniques, a large set of samples can be automatically measured, saving time and providing more objective information [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

et al. 2021

View full text Add to dashboard Cite

Yield assessment has been identified as critical topic for grape and wine industry. Computer vision has been applied for assessing yield, but the accuracy was greatly affected by fruit occlusion affected by leaves and other plant organs. The objective of this work was the consistent, continuous evaluation of the impact of leaf occlusions in different commercial vineyard plots at different defoliation stages. RGB (red, green and blue) images from five Tempranillo (Vitis vinifera L.) vineyards were manually acquired using a digital camera under field conditions at three different levels of defoliation: no defoliation, partial defoliation and full defoliation. Computer vision was used for the automatic detection of different canopy features, and for the calibration of regression equations for the prediction of yield computed per vine segment. Leaf occlusion rate (berry occlusion affected by leaves) was computed by machine vision in no defoliated vineyards. As occlusion rate increased, R2 between bunch pixels and yield was gradually reduced, ranging from 0.77 in low occlusion, to 0.63.

show abstract

Section: Introductionmentioning

confidence: 99%

“…The capabilities of an artificial vision system go beyond the limited human capacity to evaluate long-term processes objectively and provide valuable data to take decisions. Machine vision systems are being used to automate inspection tasks in agriculture and food processing [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Similarly, the work from Siebers et al (2018) has demonstrated that proximal sensing in viticulture could provide useful information on canopy architecture; the authors developed a proximal sensing platform, the Grover, equipped with LiDAR but with the capability to host and test multiple sensors. Diago et al (2019) proposed the use of an on-the-go system to collect RGB images for the assessment of grapevine canopy FIGURE 6. A comparison of different remote sensing approaches for extracting the grapevine canopy.…”

Section: Resultsmentioning

confidence: 99%

UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications

et al. 2020

View full text Add to dashboard Cite

Aim: To analyse unmanned aerial vehicle (UAV)-based imagery to assess canopy structural changes after the application of different canopy management practices in the vineyard.Methods and results: Four different canopy management practices: i–ii) leaf removal within the bunch zone (eastern side/both eastern and western sides), iii) bunch thinning and iv) shoot trimming were applied to grapevines at veraison, in a commercial Cabernet-Sauvignon vineyard in McLaren Vale, South Australia. UAV-based imagery captures were taken: i) before the canopy treatments, ii) after the treatments and iii) at harvest to assess the treatment outcomes. Canopy volume, projected canopy area and normalized difference vegetation index (NDVI) were derived from the analysis of RGB and multispectral imagery collected using the UAV. Plant area index (PAI) was calculated using the smartphone app VitiCanopy as a ground-based measurement for comparison with UAV-derived measurements. Results showed that all three types of UAV-based measurements detected changes in the canopy structure after the application of canopy management practices, except for the bunch thinning treatment. As expected, ground-based PAI was the only technique to effectively detect internal canopy structure changes caused by bunch thinning. Canopy volume and PAI were found to better detect variations in canopy structure compared to NDVI and projected canopy area. The latter were negatively affected by the interference of the trimmed shoots left on the ground.Conclusions: UAV-based tools can provide accurate assessments to some canopy management outcomes at the vineyard scale. Among different UAV-based measurements, canopy volume was more sensitive to changes in canopy structure, compared to NDVI and projected canopy area, and demonstrated a greater potential to assess the outcomes of a range of canopy management practices. Significance and impact of the study: Canopy management practices are widely applied to regulate canopy growth, improve grape quality and reduce disease pressure in the bunch zone. Being able to detect major changes in canopy structure, with some limitations when the practice affects the internal structure (i.e., bunch thinning), UAV-based imagery analysis can be used to measure the outcome of common canopy management practices and it can improve the efficiency of vineyard management.

show abstract

“…Image acquisition was performed at night using an artificial illumination system mounted onto the mobile platform in order to obtain homogeneity on the illumination of the vines and to separate the vine under evaluation from the vines of the opposite row. An all-terrain vehicle (ATV) (Trail Boss 330, Polaris Industries, Medina, Minnesota, USA) was modified to incorporate all components as described in the work of Diago et al [23] (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions

Palacios

Diago

Tardáguila

2019

Sensors

View full text Add to dashboard Cite

Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine industry. The goal of this work was to develop a new, non-invasive method based on the combination of computer vision and machine learning technology for cluster compactness assessment under field conditions from on-the-go red, green, blue (RGB) image acquisition. A mobile sensing platform was used to automatically capture RGB images of grapevine canopies and fruiting zones at night using artificial illumination. Likewise, a set of 195 clusters of four red grapevine varieties of three commercial vineyards were photographed during several years one week prior to harvest. After image acquisition, cluster compactness was evaluated by a group of 15 experts in the laboratory following the International Organization of Vine and Wine (OIV) 204 standard as a reference method. The developed algorithm comprises several steps, including an initial, semi-supervised image segmentation, followed by automated cluster detection and automated compactness estimation using a Gaussian process regression model. Calibration (95 clusters were used as a training set and 100 clusters as the test set) and leave-one-out cross-validation models (LOOCV; performed on the whole 195 clusters set) were elaborated. For these, determination coefficient (R2) of 0.68 and a root mean squared error (RMSE) of 0.96 were obtained on the test set between the image-based compactness estimated values and the average of the evaluators’ ratings (in the range from 1–9). Additionally, the leave-one-out cross-validation yielded a R2 of 0.70 and an RMSE of 1.11. The results show that the newly developed computer vision based method could be commercially applied by the wine industry for efficient cluster compactness estimation from RGB on-the-go image acquisition platforms in commercial vineyards.

show abstract

On‐the‐go assessment of vineyard canopy porosity, bunch and leaf exposure by image analysis

Cited by 26 publications

References 45 publications

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

Impact of Leaf Occlusions on Yield Assessment by Computer Vision in Commercial Vineyards

UAV and ground-based imagery analysis detects canopy structure changes after canopy management applications

A Non-Invasive Method Based on Computer Vision for Grapevine Cluster Compactness Assessment Using a Mobile Sensing Platform under Field Conditions

Contact Info

Product

Resources

About