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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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Related in: MedlinePlus

Correlation of the volume of the single ears in relation to the total ear weight (A), the total kernel weight (B) and the number of kernels (C).
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Figure 6: Correlation of the volume of the single ears in relation to the total ear weight (A), the total kernel weight (B) and the number of kernels (C).

Mentions: After the classification of different plant organs quantitative plant parameters were deduced from 3D laserscans. An alpha shape volume estimation was applied to the organ regions. This method enabled an easy and fast way for volume estimation and an accurate description of the concave wheat ears. These parameters were related to manually assessed yield parameters. Significant correlations were found between the measured ear volume and de facto yield parameters. The parameters total ear weight, total kernel weight and number of kernels showed high correlations of R2 = 0.71, R2 = 0.66, and R2=0.81, respectively (see Figure 6).


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)

Correlation of the volume of the single ears in relation to the total ear weight (A), the total kernel weight (B) and the number of kernels (C).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Correlation of the volume of the single ears in relation to the total ear weight (A), the total kernel weight (B) and the number of kernels (C).
Mentions: After the classification of different plant organs quantitative plant parameters were deduced from 3D laserscans. An alpha shape volume estimation was applied to the organ regions. This method enabled an easy and fast way for volume estimation and an accurate description of the concave wheat ears. These parameters were related to manually assessed yield parameters. Significant correlations were found between the measured ear volume and de facto yield parameters. The parameters total ear weight, total kernel weight and number of kernels showed high correlations of R2 = 0.71, R2 = 0.66, and R2=0.81, respectively (see Figure 6).

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