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Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping.

Paulus S, Dupuis J, Mahlein AK, Kuhlmann H - BMC Bioinformatics (2013)

Bottom Line: We introduced an approach using surface feature histograms for automated plant organ parameterization.Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained.This approach was found to be independent of the point to point distance and applicable to multiple plant species.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de

ABSTRACT

Background: Laserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.

Results: A surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.

Conclusion: We introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.

Highlights: • Automatic classification of plant organs using geometrical surface information• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants• Analysis of 3D-data requirements for automated plant organ classification.

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Laserscanner-measuring arm combination (A) and 3D data of grapevine (B).
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Figure 7: Laserscanner-measuring arm combination (A) and 3D data of grapevine (B).

Mentions: 3D point clouds were acquired by a high resolution laserscanner (Perceptron Scan Works V5, Perceptron Inc., Plymouth MI, USA) using an active laser triangulation method. The system was mounted on an articulated measuring arm (Romer Infinite 2.0 (2.8m Version), Hexagon Metrology Services Ltd., London UK) to enable an automated fusion of single scan-lines, coming from different points of view (Figure 7A). Thus, point clouds could be acquired with a minimum of occlusion (Figure 7B). The 3D laserscanner has a measuring field of 110 mm×105 mm, providing a point reproducibility of 0.088 mm. It was chosen due to its high point resolution, leading to highly reliable point measurements. The high resolution and accuracy is fundamental for organ specific classification and precise measurement of plant deformation as it is focused in phenotyping.


Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping.

Paulus S, Dupuis J, Mahlein AK, Kuhlmann H - BMC Bioinformatics (2013)

Laserscanner-measuring arm combination (A) and 3D data of grapevine (B).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750309&req=5

Figure 7: Laserscanner-measuring arm combination (A) and 3D data of grapevine (B).
Mentions: 3D point clouds were acquired by a high resolution laserscanner (Perceptron Scan Works V5, Perceptron Inc., Plymouth MI, USA) using an active laser triangulation method. The system was mounted on an articulated measuring arm (Romer Infinite 2.0 (2.8m Version), Hexagon Metrology Services Ltd., London UK) to enable an automated fusion of single scan-lines, coming from different points of view (Figure 7A). Thus, point clouds could be acquired with a minimum of occlusion (Figure 7B). The 3D laserscanner has a measuring field of 110 mm×105 mm, providing a point reproducibility of 0.088 mm. It was chosen due to its high point resolution, leading to highly reliable point measurements. The high resolution and accuracy is fundamental for organ specific classification and precise measurement of plant deformation as it is focused in phenotyping.

Bottom Line: We introduced an approach using surface feature histograms for automated plant organ parameterization.Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained.This approach was found to be independent of the point to point distance and applicable to multiple plant species.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de

ABSTRACT

Background: Laserscanning recently has become a powerful and common method for plant parameterization and plant growth observation on nearly every scale range. However, 3D measurements with high accuracy, spatial resolution and speed result in a multitude of points that require processing and analysis. The primary objective of this research has been to establish a reliable and fast technique for high throughput phenotyping using differentiation, segmentation and classification of single plants by a fully automated system. In this report, we introduce a technique for automated classification of point clouds of plants and present the applicability for plant parameterization.

Results: A surface feature histogram based approach from the field of robotics was adapted to close-up laserscans of plants. Local geometric point features describe class characteristics, which were used to distinguish among different plant organs. This approach has been proven and tested on several plant species. Grapevine stems and leaves were classified with an accuracy of up to 98%. The proposed method was successfully transferred to 3D-laserscans of wheat plants for yield estimation. Wheat ears were separated with an accuracy of 96% from other plant organs. Subsequently, the ear volume was calculated and correlated to the ear weight, the kernel weights and the number of kernels. Furthermore the impact of the data resolution was evaluated considering point to point distances between 0.3 and 4.0 mm with respect to the classification accuracy.

Conclusion: We introduced an approach using surface feature histograms for automated plant organ parameterization. Highly reliable classification results of about 96% for the separation of grapevine and wheat organs have been obtained. This approach was found to be independent of the point to point distance and applicable to multiple plant species. Its reliability, flexibility and its high order of automation make this method well suited for the demands of high throughput phenotyping.

Highlights: • Automatic classification of plant organs using geometrical surface information• Transfer of analysis methods for low resolution point clouds to close-up laser measurements of plants• Analysis of 3D-data requirements for automated plant organ classification.

Show MeSH
Related in: MedlinePlus