Image-Based Machine Learning Characterizes Root Nodule in Soybean Exposed to Siliconopen access
- Chung, Yong Suk; Lee, Unseok; Heo, Seong; Silva, Renato Rodrigues; Na, Chae-In; Kim, Yoonha
- Issue Date
- FRONTIERS MEDIA SA
- phenomics; root phenotype; nodule count; nodule size; legume; machine learning; image process; high-throughput phenotyping
- FRONTIERS IN PLANT SCIENCE, v.11
- Journal Title
- FRONTIERS IN PLANT SCIENCE
- Silicon promotes nodule formation in legume roots which is crucial for nitrogen fixation. However, it is very time-consuming and laborious to count the number of nodules and to measure nodule size manually, which led nodule characterization not to be study as much as other agronomical characters. Thus, the current study incorporated various techniques including machine learning to determine the number and size of root nodules and identify various root phenotypes from root images that may be associated with nodule formation with and without silicon treatment. Among those techniques, the machine learning for characterizing nodule is the first attempt, which enabled us to find high correlations among root phenotypes including root length, number of forks, and average link angles, and nodule characters such as number of nodules and nodule size with silicon treatments. The methods here could greatly accelerate further investigation such as delineating the optimal concentration of silicon for nodule formation.
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- 농업생명과학대학 > 농학과 > Journal Articles
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