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

Iñíguez, Rubén; Palacios, Fernando Fernández; Barrio, Ignacio; Hernández, Inés; Gutiérrez, Salvador; Tardáguila, Javier

doi:10.3390/agronomy11051003

Cited by 12 publications

(9 citation statements)

References 24 publications

(64 reference statements)

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“…The dataset used in this work, which encompasses six subsets, corresponding to six cvs. collected in three vineyard plots, presents significant variability, with similar previously reported magnitudes [39], an important feature required to validate our hypothesis. Previous research reports that bunch number explains approximately 60-70% of the seasonal variation in vine yield [8].…”

Section: Yield Estimatorssupporting

confidence: 86%

“…Furthermore, vineyards with very dense canopies are not usually considered. In such cases, only a small fraction of bunches is visible and not necessarily correlated with vine yield [39], and defoliation is not always a common practice (e.g., warm climate viticulture). The visibility of yield components along the vine growing cycle has been previously explored [40], and bunch occlusion ratios between 50% and 75% at the veraison stage were observed.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Manual vineyard yield estimation approaches are easy to use and can provide relevant information at early stages of plant development. However, such methods are subject to spatial and temporal variability as they are sample-based and dependent on historical data. The present work aims at comparing the accuracy of a new non-invasive and multicultivar, image-based yield estimation approach with a manual method. Non-disturbed grapevine images were collected from six cultivars, at three vineyard plots in Portugal, at the very beginning of veraison, in a total of 213 images. A stepwise regression model was used to select the most appropriate set of variables to predict the yield. A combination of derived variables was obtained that included visible bunch area, estimated total bunch area, perimeter, visible berry number and bunch compactness. The model achieved an R2 = 0.86 on the validation set. The image-based yield estimates outperformed manual ones on five out of six cultivar data sets, with most estimates achieving absolute errors below 10%. Higher errors were observed on vines with denser canopies. The studied approach has the potential to be fully automated and used across whole vineyards while being able to surpass most bunch occlusions by leaves.

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Section: Yield Estimatorssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…In the present work, the authors opted to address yield estimation by exploring the vine bunch projected area; however, occlusions can occur regardless of the considered yield component. Vine-occlusions appear to be the main challenge (Victorino et al, 2019;Íñiguez et al, 2021), particularly leaf-occlusions, especially in dense canopies that present numerous leaf layers (Smart and Robinson, 1991).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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“…It is also noted that the error increases with canopy density. Although fruit area prediction potential is still highly impacted by foliage, moderate defoliation can help obtain better correlation [152]. In practice, the conditions in a vineyard are more difficult: smaller green grapes, high canopy occlusion, a natural background, variable lighting, etc.…”

Section: Recent Progress and Problems To Solvementioning

confidence: 99%

Computer Vision and Deep Learning for Precision Viticulture

Mohimont

Alin

Rondeau³

et al. 2022

Agronomy

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During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task.

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

Cited by 12 publications

References 24 publications

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

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

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

Computer Vision and Deep Learning for Precision Viticulture

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