Overcoming the challenge of bunch occlusion by leaves for vineyard yield estimation using image analysis

Abstract

Accurate yield estimation is of utmost importance for the entire grape and wine production chain, yet it remains an extremely challenging process due to high spatial and temporal variability in vineyards. Recent research has focused on using image analysis for vineyard yield estimation, with one of the major obstacles being the high degree of occlusion of bunches by leaves. This work uses canopy features obtained from 2D images (canopy porosity and visible bunch area) as proxies for estimating the proportion of occluded bunches by leaves to enable automatic yield estimation on non-disturbed canopies. Data was collected from three grapevine varieties, and images were captured from 1 m segments at two phenological stages (veraison and full maturation) in non-defoliated and partially defoliated vines. Visible bunches (bunch exposure; BE) varied between 16 and 64 %. This percentage was estimated using a multiple regression model that includes canopy porosity and visible bunch area as predictors, yielding a R2 between 0.70 and 0.84 on a training set composed of 70 % of all data, showing an explanatory power 10 to 43 % higher than when using the predictors individually. A model based on the combined data set (all varieties and phenological stages) was selected for BE estimation, achieving a R2 = 0.80 on the validation set. This model did not show validation metrics differences when applied on data collected at veraison or full maturation, suggesting that BE can be accurately estimated at any stage. Bunch exposure was then used to estimate total bunch area (tBA), showing low errors (< 10 %) except for the variety Arinto, which presents specific morphological traits such as large leaves and bunches. Finally, yield estimation computed from estimated tBA presented a very low error (0.2 %) on the validation data set with pooled data. However, when performed on every single variety, the simplified approach of area-to-mass conversion was less accurate for the variety Syrah. The method demonstrated in this work is an important step towards a fully automated non-invasive yield estimation approach, as it offers a solution to estimate bunches that are not visible to imaging sensors.

Comparing a New Non-Invasive Vineyard Yield Estimation Approach Based on Image Analysis with Manual Sample-Based Methods

“…Previously, canopy porosity has been estimated via image analysis [43] and presents results similar to the ones obtained via classical approaches [44]. Visible bunch area and canopy porosity have been used to estimate the percentage of exposed bunches [42]. This percentage was then used to compute the total bunch area, which, in turn, was used as a proxy of vine yield by means of a simplified area-to-mass conversion.…”

“…This work showed promising results in their synthetic data but inconclusive in real vine images. In recent research, our team showed that bunch occlusion by leaves could be surpassed by considering other canopy traits, besides yield components, such as canopy porosity [42]. Previously, canopy porosity has been estimated via image analysis [43] and presents results similar to the ones obtained via classical approaches [44].…”

“…Tasks 1 to 4 were performed with the software Image J ®®® (v1.53k, National Institutes of Health, EUA). Image features were selected based on previous research [42,45].…”

“…The final yield was compared to the output of two different methods of yield estimation: (i) the manual method, based on bunch counts, conventionally used in commercial vineyards and thoroughly described as the traditional method in [7]; (ii) an image analysis method following recent research [42], followed by a conversion of bunch area into yield based on several yield-predicting vine traits. The image analysis method described in Victorino et al [42] is based on the assumption that a relationship between canopy porosity and bunch exposure exists. The method uses a combination of canopy porosity and visible bunch pixels to estimate the occluded fraction of the bunch area.…”

“…Yield estimation via image analysis was obtained according to two different approaches, both based on a previously reported methodology [42]. In that methodology, vBA and POR were used to estimate the fraction of bunches occluded by leaves and, consequently, the total bunch projected area (TBA).…”