A robust automated flower estimation system for grape vines

Liu, Scarlett; Li, Xuesong; Wu, Hongkun; Xin, Bolai; Tang, Julie; Petrie, Paul R.; Whitty, Mark

doi:10.1016/j.biosystemseng.2018.05.009

Cited by 37 publications

(32 citation statements)

References 14 publications

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“…As a result, a case-by-case validation step is required for each variety, which is unpractical for wide research studies. Similar problems are expected with other methods for estimating the number of flowers in grapevine inflorescences based on 2D image analysis that have been recently proposed[30,32,46]. They are surely useful for certain purposes in the varieties tested, but not in this case because the very different type of inflorescences in the different varieties studied.For that reason, we opted for an alternative method in 2016 and 2017, based on the direct counting of the calyptras.…”

supporting

confidence: 54%

“…Instead, counting the number of flower caps or calyptras (fused petals) collected in a bag is a non-destructive valid method, because each flower releases one calyptra at blooming [9,17,18,23], which can be delayed. On the other hand, new image analyses tools have been recently developed for estimating the number of flowers from 2D images of the inflorescences, although they have usually been tested in a limited set of varieties [28][29][30][31][32][33].…”

Section: In Many Cases Seedless Berries Andmentioning

confidence: 99%

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Cultivated Grapevine Displays a Great Diversity for Reproductive Performance Variables

2019

Crop Breed Genet Genom.

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Background: There are thousands of grapevine varieties that display a wide range of variation for traits like grape use (wine, table grape or both), color or ripening time, but little is known about their reproductive performance, especially flowering and fruitset (conversion from flower to fruit). Works focused at the study of these traits in grapevine evaluated one or few varieties and used different methodologies making comparisons difficult. This study aimed to characterize the reproductive performance of 120 varieties and its stability over two seasons using a precise methodology. Methods: Reproductive performance was determined through counting flowers and berries in the same inflorescences/bunches (10 per variety), for which a new methodology of image analysis of scanned calyptras was developed. Varieties were classified according to their reproductive performance. Results: A great diversity was found for most variables including fruitset and number of flowers. Large differences between varieties were observed both in values and in stability among seasons. The varieties clustered in three main classes that displayed significant differences not only for the reproductive performance variables used for the clustering but for most the variables studied. Varieties in these classes showed a nonrandom distribution regarding the grape use and the genetic structure based on molecular markers. Conclusions: This is the largest study of reproductive performance variables such as fruitset ever done in grapevine. It provides specific values for many varieties for the first time, useful for breeding programs. The clustering based on these variables is related with those based on use and geographical origin.

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supporting

confidence: 54%

Section: In Many Cases Seedless Berries Andmentioning

confidence: 99%

Cultivated Grapevine Displays a Great Diversity for Reproductive Performance Variables

2019

Crop Breed Genet Genom.

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“…Another work by does the same for images taken with a camera in the field, while Aquino et al (2015b) provide a method to segment grapevine inflorescences. Liu et al (2018) capture images in the field with a black backing board that is used to provide a controlled background.…”

Section: Phenotyping Based On Imagesmentioning

confidence: 99%

Constraint-based automated reconstruction of grape bunches from 3D range data for high-throughput phenotyping

Mack

Rist

Herzog

et al. 2020

Biosystems Engineering

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“…Computer vision and image processing technology enables low-cost, automated information extraction and its analysis from images taken using a digital camera. This technology is being used in viticulture to estimate key parameters such as vine pruning weight [12,13], the number of flowers per inflorescence [14,15], canopy features [16], or yield [17,18], as well as to provide relevant information to grape harvesting robots [19,20].…”

Section: Introductionmentioning

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

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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.

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A robust automated flower estimation system for grape vines

Cited by 37 publications

References 14 publications

Cultivated Grapevine Displays a Great Diversity for Reproductive Performance Variables

Cultivated Grapevine Displays a Great Diversity for Reproductive Performance Variables

Constraint-based automated reconstruction of grape bunches from 3D range data for high-throughput phenotyping

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

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