title: Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping.
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.
  
  
  CONCLUSIONS
  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.
  
  
  CONCLUSIONS
  • 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.
authors.affiliation: Institute of Geodesy and Geoinformation - Professorship of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, Germany. paulus@igg.uni-bonn.de
authors.forename: Stefan
authors.initials: S
authors.lastname: Paulus
authors.forename: Jan
authors.initials: J
authors.lastname: Dupuis
authors.forename: Anne-Katrin
authors.initials: AK
authors.lastname: Mahlein
authors.forename: Heiner
authors.initials: H
authors.lastname: Kuhlmann
mesh_terms: D021621:Imaging, Three-Dimensional / Q000379:methods*; D007834:Lasers; D010641:Phenotype; D018515:Plant Leaves / Q000145:classification; D018547:Plant Stems / Q000145:classification; D018514:Plant Structures / Q000033:anatomy & histology / Q000145:classification*; D014908:Triticum / Q000033:anatomy & histology
references.citation: Funct Plant Biol. 2012 Nov;39(11):813-820
references.pmid: 32480832
references.citation: New Phytol. 2011 Aug;191(3):895-907
references.pmid: 21569033
references.citation: Sensors (Basel). 2011;11(2):2166-2174
references.pmid: 22319403
references.citation: Funct Plant Biol. 2012 Nov;39(11):870-877
references.pmid: 32480837
references.citation: Funct Plant Biol. 2013 Jul;40(7):677-689
references.pmid: 32481140
references.citation: Funct Plant Biol. 2008 Dec;35(10):1059-1069
references.pmid: 32688854
references.citation: Front Physiol. 2012 Jun 01;3:180
references.pmid: 22675308
references.citation: J Exp Bot. 2007;58(4):881-98
references.pmid: 17030540
references.citation: J Exp Bot. 2000 Sep;51(350):1515-29
references.pmid: 11006303
references.citation: BMC Plant Biol. 2012 May 03;12:63
references.pmid: 22553969
references.citation: Trends Plant Sci. 2011 Dec;16(12):635-44
references.pmid: 22074787
references.citation: J Exp Bot. 2010 May;61(8):2101-15
references.pmid: 19995824
references.citation: IEEE Trans Neural Netw. 1999;10(5):988-99
references.pmid: 18252602