Laserscanning recently has become a powerful and common method for plant parameterization andplant growth observation on nearly every scale range. However, 3D measurements with highaccuracy, spatial resolution and speed result in a multitude of points that require processing andanalysis.
The primary objective of this research has been to establish a reliable and fast technique forhigh throughput phenotyping using differentiation, segmentation and classification of single plantsby a fully automated system. In this report, we introduce a technique for automated classification ofpoint 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-uplaserscans of plants.Local geometric point features describe class characteristics, which were usedto distinguish among different plant organs. This approach has been proven and tested on severalplant species.Grapevine stems and leaves were classified with an accuracy of up to 98%. Theproposed 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 earvolume was calculated and correlated to the ear weight, the kernel weights and the number ofkernels. Furthermore the impact of the data resolution was evaluated considering point to pointdistances 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 organparameterization.Highly reliable classification results of about 96% for the separation of grapevineand wheat organs have been obtained. This approach was found to be independent of the point topoint distance and applicable to multiple plant species.Its reliability, flexibility and its high order ofautomation make this method well suited for the demands of high throughput phenotyping.