Automated 3D reconstruction of grape cluster architecture from sensor data for efficient phenotyping

Schöler, Florian; Steinhage, Volker

doi:10.1016/j.compag.2015.04.001

Cited by 41 publications

(36 citation statements)

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“…Measurement of vegetative parameters like plant and canopy height, leaf parameters, fruit detection, and characterization of crop and fruit plants have been described (An overview is given by Paulus [21]). Regarding the phenotypically complex and highly varied characteristics of grape bunches: In different studies, 3D scanning was applied under controlled lab conditions in order to obtain the full 360 • structure of the bunches [8,[22][23][24][25][26][27]. The limitation of several 3D methods is a reduced throughput, which restricts the number of grapes that can be used for analysis.…”

Section: Introductionmentioning

confidence: 99%

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

et al. 2019

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In grapevine breeding, loose grape bunch architecture is one of the most important selection traits, contributing to an increased resilience towards Botrytis bunch rot. Grape bunch architecture is mainly influenced by the berry number, berry size, the total berry volume, and bunch width and length. For an objective, precise, and high-throughput assessment of these architectural traits, the 3D imaging sensor Artec® Spider was applied to gather dense point clouds of the visible side of grape bunches directly in the field. Data acquisition in the field is much faster and non-destructive in comparison to lab applications but results in incomplete point clouds and, thus, mostly incomplete phenotypic values. Therefore, lab scans of whole bunches (360°) were used as ground truth. We observed strong correlations between field and lab data but also shifts in mean and max values, especially for the berry number and total berry volume. For this reason, the present study is focused on the training and validation of different predictive regression models using 3D data from approximately 2000 different grape bunches in order to predict incomplete bunch traits from field data. Modeling concepts included simple linear regression and machine learning-based approaches. The support vector machine was the best and most robust regression model, predicting the phenotypic traits with an R2 of 0.70–0.91. As a breeding orientated proof-of-concept, we additionally performed a Quantitative Trait Loci (QTL)-analysis with both the field modeled and lab data. All types of data resulted in joint QTL regions, indicating that this innovative, fast, and non-destructive phenotyping method is also applicable for molecular marker development and grapevine breeding research.

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Section: Introductionmentioning

confidence: 99%

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

et al. 2019

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“…25,26 Ivorra et al, 25 created a 3D model from only one face of the cluster. On the other hand, 3D laser scanning has recently been used to create more accurate models of full clusters, 27 but it has not yet been applied in a multi-cultivar framework.…”

Section: Introductionmentioning

confidence: 99%

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters

et al. 2016

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BACKGROUND Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two‐dimensional (2D) and three‐dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. RESULTS The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R2 = 84.5 and 71.1%, respectively). CONCLUSION The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry

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“…Ivorra et al (2015) propose a three dimensional computer vision approach to assessing grape yield components based on new 3D descriptors. More advanced methodologies applied in viticulture is found in the studies of Nuske et al (2011), Liu et al (2013), Nuske et al (2014), Aquino et al (2015), , Font et al (2015) and Schöler et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of the flower buttons per inflorescences of grapevine (Vitis vinifera L.) by image auto-assessment processing

Benmehaia¹,

Khedidja²,

Bentchikou³

2016

Afr. J. Agric. Res.

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The aim of this research is to develop a reliable tool by a special method, combining image processing based on watershed algorithm, and a predictive model to estimate automatically the flowers number per inflorescence. Eighty images of Vitis vinifera L. inflorescence (the Cardinal cultivar) were processed. Watershed algorithm was used for the image processing and this was followed by statistical analysis that provides robust predictive estimation of the flower button number. The results show a robust estimation, compared to manual flowers counting, with strong correlation. The developed algorithm shows that the watershed algorithm was able to provide an automatic assessment of the flower button number in the inflorescence. The method used is more robust and provides a more significant level compared with recent studies. In the applied research in viticulture, it is crucial to improve knowledge of yield forecasting and to study the fruit set rates estimation. The technique is used to determine, with a higher significance level, the fruitiness rate of grapevine at the early stage of flowering.

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Automated 3D reconstruction of grape cluster architecture from sensor data for efficient phenotyping

Cited by 41 publications

References 50 publications

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

Combination of an Automated 3D Field Phenotyping Workflow and Predictive Modelling for High-Throughput and Non-Invasive Phenotyping of Grape Bunches

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters

Estimation of the flower buttons per inflorescences of grapevine (Vitis vinifera L.) by image auto-assessment processing

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