Developing a system for three-dimensional quantification of root traits of rice seedlings

A plant's root system architecture (RSA) is the spatial configuration of its roots; the RSA of Oryza sativa L. (rice) shows a high degree of diversity. The RSA of rice should be quantified with high accuracy to understand the relationship between the RSA and functionality of rice roots. This study developed an imaging system for three-dimensional (3D) quantification of the RSA of rice. In this study, rice seedlings of 20 varieties were cultivated in glass tubes filled with transparent gellan gum for 10 days after germination. A servomotor-controlled camera captured two-dimensional side-view images of the seedlings at predetermined angles. A convolutional neural network classifier was then developed to segment the roots from the background. Subsequently, 3D images of the rice roots were constructed, and the phenotypic traits were quantified from the 3D images. Results displayed a high degree of diversity in the traits of the 20 varieties. A ground truth with designed parameters was used to validate the accuracy of this system. Analysis results indicated that the developed system was 98.3%, 97.6%, and 93.3% accurate regarding the primary root length, total root length, and root volume, respectively.