Abstract
Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor is able to replace an expensive laser scanner in many plant phenotyping scenarios.
Highlights
The importance of automated plant phenotyping has been addressed in many publications in recent years [1,2,3]
The potential of the low-cost sensors was tested in different measuring scenarios
Specific plant parameters for plant phenotyping were derived from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears (Figure 3) and evaluated
Summary
The importance of automated plant phenotyping has been addressed in many publications in recent years [1,2,3]. To improve the accuracy and efficiency of phenotyping processes, different types of sensor techniques, such as 3D laser scanning techniques, RGB-cameras, hyperspectral and thermal cameras or chlorophyll fluorescence imaging, have been introduced [2,4,5]. In this context, the characterization of plant features, like the morphology, physiology and performance of a genotype under specific environmental conditions, is expected to increase the efficiency of plant breeding [6]. Reliable statements about plant responses to environmental conditions are only possible if a significant number of plants is included in a study [10,11,12]; sensor techniques with the potential for an implementation in high throughput routines are required
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