Assessment of Vineyard Canopy Porosity Using Machine Vision

Canopy porosity is an important viticultural factor because canopy gaps favor fruit exposure and air circulation, both of which benefit fruit quality and health. Point quadrat analysis (PQA) is standard for assessing canopy gaps but has limited utility because the method is laborious and time consuming. A new, objective, noninvasive, image-based method was developed and compared with PQA to assess the percent canopy gaps in vineyards with diverse viticultural conditions and grape varieties in New Zealand, Croatia, and Spain. The determination coefficient (R2) of the regressions between the percent gaps using both methods exceeded 0.90 (p < 0.05) at each site, and R2 of the global regression was 0.93 (p < 0.05). The time of day and side of the canopy photographed did not significantly affect the performance of the algorithm. With this new image-based assessment method, canopy management may be optimized to configure a desired amount of canopy gaps and thereby improve fruit quality and health.

[1]  Justine E. Vanden Heuvel,et al.  Enhancing the Precision and Spatial Acuity of Point Quadrat Analyses via Calibrated Exposure Mapping , 2008, American Journal of Enology and Viticulture.

[2]  José Blasco,et al.  A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis , 2014 .

[3]  Stefano Poni,et al.  Impact of Early Leaf Removal on Yield and Fruit and Wine Composition of Vitis vinifera L. Graciano and Carignan , 2010, American Journal of Enology and Viticulture.

[4]  J. Marois,et al.  Influence of leaf removal from the fruit cluster zone on the components of evaporative potential within grapevine canopies , 1990 .

[5]  M. Diago,et al.  Impact of Prebloom and Fruit Set Basal Leaf Removal on the Flavonol and Anthocyanin Composition of Tempranillo Grapes , 2012, American Journal of Enology and Viticulture.

[6]  K. K. Ganić,et al.  Effects of fruit zone leaf removal on the concentrations of phenolic and organic acids in Istrian Malvasia grape juice and wine. , 2012 .

[7]  Nuria Aleixos,et al.  Assessment of cluster yield components by image analysis. , 2015, Journal of the science of food and agriculture.

[8]  María-Paz Diago,et al.  Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions , 2012, Sensors.

[9]  Image-Assisted Gap Estimation, a Simple Method for Measuring Grapevine Leaf Canopy Density , 2011 .

[10]  M. Balehegn,et al.  Training reduced subjectivity of comparative yield method of estimation of grassland biomass , 2016 .

[11]  N. Polunin,et al.  Appraisal of visual assessments of habitat complexity and benthic composition on coral reefs , 2007 .

[12]  R. Smart.  INFLUENCE OF LIGHT ON COMPOSITION AND QUALITY OF GRAPES , 1987 .

[13]  G. Dunn,et al.  Yield prediction from digital image analysis: A technique with potential for vineyard assessments prior to harvest , 2008 .

[14]  Stefano Poni,et al.  Mechanical yield regulation in winegrapes: comparison of early defoliation and crop thinning , 2012 .

[15]  Gary G. Grove,et al.  Powdery Mildew Severity as a Function of Canopy Density: Associated Impacts on Sunlight Penetration and Spray Coverage , 2011, American Journal of Enology and Viticulture.

[16]  R. E. Smart,et al.  Sunlight into wine: a handbook for winegrape canopy management. , 1991 .

[17]  Javier Tardáguila,et al.  Effects of Timing of Manual and Mechanical Early Defoliation on the Aroma of Vitis vinifera L. Tempranillo Wine , 2010, American Journal of Enology and Viticulture.

[18]  A. Reynolds,et al.  Influence of Fruit Microclimate on Monoterpene Levels of Gewürztraminer , 1989, American Journal of Enology and Viticulture.

[19]  Alexandre Escolà,et al.  Influence of the scanned side of the row in terrestrial laser sensor applications in vineyards: practical consequences , 2014, Precision Agriculture.

[20]  W. Kliewer.  Effect of High Temperatures during the Bloom-Set Period on Fruit-Set, Ovule Fertility, and Berry Growth of Several Grape Cultivars , 1977, American Journal of Enology and Viticulture.

[21]  Sanjiv Singh,et al.  Automated Visual Yield Estimation in Vineyards , 2014, J. Field Robotics.

[22]  J. Wilson,et al.  ANALYSIS OF THE SPATIAL DISTRIBUTION OF FOLIAGE BY TWO‐DIMENSIONAL POINT QUADRATS , 1959 .

[23]  M. Kitayama,et al.  Effect of high temperature on anthocyanin composition and transcription of flavonoid hydroxylase genes in ‘Pinot noir’ grapes (Vitis vinifera) , 2007 .

[24]  José Blasco,et al.  Assessment of flower number per inflorescence in grapevine by image analysis under field conditions. , 2014, Journal of the science of food and agriculture.

[25]  José Blasco,et al.  A new method for assessment of bunch compactness using automated image analysis , 2015 .

[26]  Roberta De Bei,et al.  Development of a smartphone application to characterise temporal and spatial canopy architecture and leaf area index for grapevines , 2012 .