For digital plant phenotyping huge amounts of 2D images are acquired. This is known as one part of the phenotyping bottleneck. This bottleneck can be addressed by well-educated plant analysts, huge experience and an adapted analysis software. Automated tools that only cover specific parts of this analysis pipeline are provided. During the last years this could be changed by the image processing toolbox of LemnaTec GmbH. An automated and intuitive tool for the automated analysis of huge amounts of 2D data. Various image processing pipelines like edge detectors or background foreground separators are available as well as machine learning routines for more sophisticated problems. Segmentation of single plant parts is possible for plant images on different scales from microtiter plates, petri dishes, and single plants in the greenhouse or on field scale.
Single modules can be attached to build an adapted analysis pipeline for a specific dataset and then repeatedly used for datasets of a similar plant. This enables the extraction of parameters like convex hull, height, and diameter or leaf area. For applications like the geometric parameterization of the complete plant, the classification of the ears from cereal field images using RGB cameras or 3D laser scans, or the segmentation of leaves by using hyperspectral images are possible in high throughput. Once created parameterization pipelines can be easy adapted to different plant species. Two application scenarios using this software are described in detail within this publication.
An automated running analysis pipeline for the parameterization of geometric plant parameters by using RGB photos is shown on greenhouse scale. This is based on an automated acquisition using LemnaTec conveyor systems and an adapted measuring booth. Furthermore we show the localization of plant organs by using radiometric features on images coming from crane based measuring platform on field scale. The image analysis software LemnaGrid (LemnaTec GmbH, Aachen) provides a professional tool that enables the intuitive connection of different image processing algorithms. It is adaptable for different plant types and on different scales. In this process the data processing can use different sensor data coming from RGB, 3D, hyperspectral or fluorescence imaging.