Vineyard yield estimation by automatic 3D bunch modelling in field conditions

Herrero-Huerta, Mónica; González‐Aguilera, Diego; Rodríguez‐Gonzálvez, Pablo; Hernández-López, David

doi:10.1016/j.compag.2014.10.003

Cited by 78 publications

(43 citation statements)

References 39 publications

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“…No scale deduction from known objects in the images [11,20,23] is needed. Camera calibration is done automatically without the need for user intervention.…”

Section: Methodsmentioning

confidence: 99%

“…In the meantime, the demand for access to the plants’ 3D geometry has become of significant importance for phenotyping in general. Several solutions under laboratory and field conditions employing laser scanning [13,14], time of flight cameras [15,16] structured light [17] or multi-view-stereo (MVS) approaches [18,19,20] are opening the door to 3D phenotyping. Multi-view-stereo approaches are especially suited for reconstructing the complex morphology of grapevines.…”

Section: Introductionmentioning

confidence: 99%

“…Herrero-Huerta et al [20] constructed point clouds of grapevines using their own MVS software and images of slightly defoliated grapevines taken at daytime from hand with a single consumer camera. The point clouds were metrically scaled by detecting scale targets of known size in the images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

Rose

Kicherer

Wieland

et al. 2016

Sensors

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In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.

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“…No scale deduction from known objects in the images [11,20,23] is needed. Camera calibration is done automatically without the need for user intervention.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

Rose

Kicherer

Wieland

et al. 2016

Sensors

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“…On the other hand, novel three‐dimensional (3D) technologies emerge as interesting approaches for the evaluation of cluster morphology. In the same line, the 3D reconstruction of the structure of the grapevine cluster from 2D images has also been assayed for the evaluation of different cluster attributes, including cluster compactness . Ivorra et al , created a 3D model from only one face of the cluster.…”

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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“…Zhao et al (2013) assimilated remote sensing information with a crop model using an Ensemble Kalman Filter for maize yield estimation. Herrero-Huerta et al (2015) presented vineyard yield estimation by automatic 3D bunch modelling in field conditions. Compared with the above-mentioned research on remote sensing, optical spectrum, this study was rendered convenient, economical and effective by the use of a mobile phone.…”

Section: Performance Efficiencymentioning

confidence: 99%

A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

Qian

Xing

et al. 2018

Sci. agric. (Piracicaba, Braz.)

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Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes a distribution framework consisting of the acquisition of fruit tree images, yield prediction in smartphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP), fitting circle amount (FC), average radius of the fitting circle (RC) and small polygon pixel area (SP). An individual tree yield estimation model on an ANN (Artificial Neural Network) was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji) orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups of data were validated. The R 2 value for the training datasets was 0.996 and the relative root meansquared error (RRMSE) value 0.063. The RRMSE value for the validation dataset was 0.284.Furthermore, a yield map with 80 apple trees was generated, and the space distribution of the yield was identified. It provided appreciable decision support for site-specific management.

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Vineyard yield estimation by automatic 3D bunch modelling in field conditions

Cited by 78 publications

References 39 publications

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions

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

A smartphone-based apple yield estimation application using imaging features and the ANN method in mature period

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