Towards an Automated 3D Reconstruction of Plant Architecture

Schöler, Florian; Steinhage, Volker

doi:10.1007/978-3-642-34176-2_6

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…For example, Schöler and Steinhase (2012) included automated 3D reconstruction of bunch architecture based on the analysis of rachises for the potential evaluation of bunch compactness. Bunch compactness is a three-dimensional (3D) feature, which makes the development of automated inspection systems based on images specifically adapted to this task challenging, and it may only be achieved from the indirect measures of some morphological descriptors.…”

Section: Introductionmentioning

confidence: 99%

“…Bunch compactness is a three-dimensional (3D) feature, which makes the development of automated inspection systems based on images specifically adapted to this task challenging, and it may only be achieved from the indirect measures of some morphological descriptors. For example, Schöler and Steinhase (2012) included automated 3D reconstruction of bunch architecture based on the analysis of rachises for the potential evaluation of bunch compactness.…”

Section: Introductionmentioning

confidence: 99%

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A new method for assessment of bunch compactness using automated image analysis

Cubero

Diago

Blasco

et al. 2015

Australian Journal of Grape and Wine Research

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Background and Aims Bunch compactness is a key feature determining grape and wine composition because tight bunches show a less homogeneous ripening, and are prone to greater fungal disease incidence. The Organisation Internationale de la Vigne et du Vin descriptor, the most recent method for the assessment of bunch compactness, requires visual inspection and trained evaluators, and provides subjective and qualitative values. The aim of this work was to develop a methodology based on image analysis to determine bunch compactness in a non‐invasive, objective and quantitative way. Methods and Results Ninety bunches of nine different red cultivars of Vitis vinifera L. were photographed with a colour camera, and their bunch compactness was determined by visual inspection. A predictive partial least squares (PLS) model was developed in order to estimate bunch compactness from the morphological features extracted by automated image analysis, after the supervised segmentation of the images. The PLS model showed a capability of 85.3% for predicting correctly the rating of bunch compactness. The most discriminant variables of the model were highly correlated with the tightness of the berries in the bunch (proportion of visibility of berries, rachis and holes) and with the shape of the bunch (roundness, compactness shape factor and aspect ratio). Conclusions The non‐invasive, image analysis methodology presented here enables the quantitative assessment of bunch compactness, thereby providing precise objective information for this key parameter. Significance of the Study A quantitative, objective and accurate system based on image analysis was developed as an alternative to current visual methods for the estimation of bunch compactness. This novel method could be applied to the classification of table grapes and/or at the receival point of wineries for sorting and assessment of wine grapes before vinification.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for assessment of bunch compactness using automated image analysis

Cubero

Diago

Blasco

et al. 2015

Australian Journal of Grape and Wine Research

View full text Add to dashboard Cite

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“…One approach to generate accurate representations of leafless trees is to employ a branching structure generative model to produce hypotheses as to where branching points might be located, and to use the measured data to confirm or reject these hypotheses. Schöler and Steinhage () use Relational Growth Grammars (Kniemeyer, Barczik, Hemmerling, & Kurth, ) along with Reversible Jump Markov Chain Monte Carlo sampling to generate such hypotheses. Binney and Sukhatme () as well as Friedman and Stamos () use models based on L‐Systems (Prusinkiewicz & Runions, ), and Preuksakarn et al.…”

Section: Related Workmentioning

confidence: 99%

Modeling Dormant Fruit Trees for Agricultural Automation

Medeiros

Kim

Sun

et al. 2016

Journal of Field Robotics

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Dormant pruning of fruit trees is one of the most costly and labor‐intensive activities in specialty crop production. We present a system that solves the first step in the process of automated pruning: accurately measuring and modeling the fruit trees. Our system employs a laser sensor to collect observations of fruit trees from multiple perspectives, and it uses these observations to measure parameters needed for pruning. A split‐and‐merge clustering algorithm divides the collected data into three sets of points: trunk candidates, junction point candidates, and branches. The trunk candidates and junction point candidates are then further refined by a robust fitting algorithm that models as cylinders each segment of the trunk and primary branches. In this work, we focus on measuring the diameters of the primary branches and the trunk, which are important factors in dormant pruning and can be obtained directly from the cylindrical models. We show that the results are qualitatively satisfactory using synthetic and real data. Our experiments with three synthetic and three real apple trees of two different varieties showed that the system is able to identify the primary branches with an average accuracy of 98% and estimate their diameters with an average error of 0.6 cm. Although the current implementation of the system is too slow for large‐scale practical applications (it can measure approximately two trees per hour), our study shows that the proposed approach may serve as a fundamental building block of robotic pruners in the near future.

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“…Thus, laser scanning using a close-range laser scanner and a measuring arm can be depicted to be the method of choice for high-precision 3D imaging of plants. Algorithms for automated segmentation using geometry-based surface descriptors [29] have been developed for 3D point clouds that enable an automated parametrization [33].…”

Section: Capturing the 3d Shape Of Plantsmentioning

confidence: 99%

Generation and application of hyperspectral 3D plant models: methods and challenges

Behmann

Mahlein

Paulus

et al. 2015

Machine Vision and Applications

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Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they also have been used for close-range sensing of plant canopies with a highly complex architecture. However, the complex geometry of plants and their interaction with the illumination setting severely affect the spectral information obtained. Furthermore, the spatial component of analysis results gain in importance as higher plants are represented by multiple plant organs as leaves, stems and seed pods. The combination of hyperspectral images and 3D point clouds is a promising approach to face these problems. We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. We sum up a geometric calibration method for hyperspectral pushbroom cameras using a reference object for the combination of spectral and spatial information. Furthermore, we show exemplarily new calibration and analysis methods enabled by the hyperspectral 3D models in an experiment with sugar beet plants. An improved normalization, a comparison of image and 3D analysis and the density estimation of infected surface points underline some of the new capabilities gained using this new data type. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified and modeled. In future, reflectance models can be used B Jan Behmann to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential to improve automated plant phenotyping significantly.

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Towards an Automated 3D Reconstruction of Plant Architecture

Cited by 9 publications

References 13 publications

A new method for assessment of bunch compactness using automated image analysis

A new method for assessment of bunch compactness using automated image analysis

Modeling Dormant Fruit Trees for Agricultural Automation

Generation and application of hyperspectral 3D plant models: methods and challenges

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