Abstract
Geometric three-dimensional (3D) reconstruction has emerged as a powerful tool for plant phenotyping and plant breeding. Although laser scanning is one of the most intensely used sensing techniques for 3D reconstruction projects, it still has many limitations, such as the high investment cost. To overcome such limitations, in the present study, a low-cost, novel, and efficient imaging system consisting of a red-green-blue (RGB) camera and a photonic mixer detector (PMD) was developed, and its usability for plant phenotyping was demonstrated via a 3D reconstruction of a soybean plant that contains color information. To reconstruct soybean canopies, a density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to extract canopy information from the raw 3D point cloud. Principal component analysis (PCA) and iterative closest point (ICP) algorithms were then used to register the multisource images for the 3D reconstruction of a soybean plant from both the side and top views. We then assessed phenotypic traits such as plant height and the greenness index based on the deviations of test samples. The results showed that compared with manual measurements, the side view-based assessments yielded a determination coefficient (R2) of 0.9890 for the estimation of soybean height and a R2 of 0.6059 for the estimation of soybean canopy greenness index; the top view-based assessment yielded a R2 of 0.9936 for the estimation of soybean height and a R2 of 0.8864 for the estimation of soybean canopy greenness. Together, the results indicated that an assembled 3D imaging device applying the algorithms developed in this study could be used as a reliable and robust platform for plant phenotyping, and potentially for automated and high-throughput applications under both natural light and indoor conditions.
Highlights
Soybeans are one of the main cash crops worldwide
Raw 3D point cloud data including the soybean canopy information were captured from the side view (Figure 1a) and top view (Figure 1b), respectively, by using a multisource imaging system consisting of a photonic mixer detector (PMD; model: Camcube 3.0, PMDTech Company, Siegen, Germany) and an RGB camera
The RGB image and associated 3D point cloud information were fused together to reconstruct the geometric morphology of the soybean canopies (Figure 1g,h) by executing the Principal component analysis (PCA) algorithm for a rough registration and the iterative closest point (ICP) algorithm for optimal registration between the input point cloud and reference point cloud
Summary
Soybeans are one of the main cash crops worldwide. To meet the needs of the growing human population, plant scientists and breeders must increase the productivity and yield of soybean crops, which is a substantial challenge [1]. High-throughput phenotyping platforms are essential for tracking the growth of soybean plants in the field and the contributions of these plants to both the food supply and the generation of bioenergy from their biomass [2]. Most of the plant phenotyping methods depend on observations and manual measurements with contact sensors; these methods are considered low throughput, costly, and labor-intensive [9]. These traditional techniques typically require the destruction of plant organs, which negatively affects the normal growth of the measured plants [10]. Plant phenotyping, which involves noninvasive measurements, is an emerging technology that has recently attracted the attention of researchers in the plant science and agricultural fields
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