关键词: 水稻根系, 表型组学, 无损检测, 图像分割

Abstract: The quantitative studies on morphological characteristics of rice root system are of great significance for improving farmland management, rice selective breeding and genetic improvement. In recent years, along with the rapid development of phenotype technology, image processing technology could be used to measure and analyze the growth of rice root system. At the same time, combined with fertilization, irrigation, light, temperature control and environmental monitoring technology, it has become a new technological means for rice breeding and functional genomics research. And the root system image segmentation technology is one of the key foundations for the subsequent phenotypic group analysis. The segmentation is very difficult, because the rice root system images have the characteristics of low contrast, low SNR and complex texture. Aiming at this problem, the paper firstly studied the main-branch connection algorithm, local threshold segmentation algorithm based on the morphological characteristics, and adaptive threshold segmentation based on morphological characteristics, and then performed segmentation and comparison on rice root system images. The results showed that although the main-branch connection algorithm retained a large number of root details, its structure was slightly chaotic and burr phenomenon was serious; the local threshold segmentation algorithm based on morphological features could retain more root details, but the contour fracture was more severe; adaptive threshold segmentation algorithm could improve image continuity, burr phenomenon was controlled, fine roots would not be preserved. These 2 algorithms were combined into a integrated algorithm suitable for image segmentation of rice surface root system. Thus, relatively ideal segmentation results could be obtained, which would provide important basis for follow-up studies on phenotypic traits of rice root system.

Key words: rice root system, phenomics, nondestructive testing, image segmentation