Abstract
Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.
Highlights
Soybean has been the most important cash crop in recent years; it has become an important source of food worldwide
Motivated by the desire to overcome the drawbacks mentioned above, we demonstrate in this study that it is viable to study the phenotypic traits of soybean plants using Kinect sensors based on a proximal platform in a natural environment
The image acquisition platform has been demonstrated to be capable of using Kinect sensor for data collection in a high-throughput fashion under natural light conditions
Summary
Soybean has been the most important cash crop in recent years; it has become an important source of food worldwide. Measured subtraits include the geometric traits (height, canopy breadth, and volume) and physiological information (chlorophyll, nitrogen, phosphorus, and potassium contents) of crops [6], which have great scientific value for breeders and geneticists [7,8]. These phenotypic traits are essential for quantitative analysis of genotype–environment interactions [9,10], and for optimizing field management activities such as cultivation, fertilization and irrigation [11,12]
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