Abstract
The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method's robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
Highlights
Plant phenotyping is intended to provide new insights into the complex relationships between plant genotypes and phenotypes under different environmental conditions
The average photoperiod is 12 h, photosynthetic photon flux density (PPFD) is 837~1023 μmol m-2 s-1 with an average of 969 μmol m-2 s-1, relative humidity ranged from 52% to 93% with an average of 77%, and temperatures ranged from 21°C to 33°C with an average of 26°C on 1 September 2017
The reconstruction results from the nonuniform rational B-spline (NURBS) surface fitting (Figure 6, columns 3 and 4) tended to contain overestimates, which represented where the produced surface overfits the point cloud data for a leaf surface
Summary
Plant phenotyping is intended to provide new insights into the complex relationships between plant genotypes and phenotypes under different environmental conditions. Technologies for the automatic derivation of plant phenotypic traits are indispensable [5]. Researchers have rapidly advanced plant phenotyping techniques using imaging techniques. 3D (three-dimensional) imaging technologies have been widely applied because they can measure plant physical traits more directly than 2D (two-dimensional) imaging technologies [6]. Active approaches use active sensors such as LiDAR (light detection and ranging) to directly capture a 3D point cloud that represents the coordinates of each part of the plant in 3D space [8, 9]. Passive approaches use passive sensors such as cameras to generate a 3D point cloud that is inferred from a set of 2D images captured from multiple perspectives [10,11,12]
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