벼 높이 시계열 분석 최적화를 위한 데이터 가공 방법 비교 분석
Comparative analysis of data processing methods for optimizing timeseries analysis of rice plant height
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초록
In this study, we developed and validated an optimized phenotypic analysis method using time-series data collected throughout the full growth cycle of 96 rice cultivars. Height growth curves were compared across three phenotyping tools (ImageJ, OpenCV, and PlantCV), each of which showed distinct performance characteristics at different stages of rice growth. ImageJ displayed significant variability in early growth stages, while OpenCV suffered from decreased accuracy during later stages. PlantCV, however, provided stable and consistent results across all stages, making it the most reliable tool for this phenotypic analysis. We also examined the effects of replicate sampling, camera angle selection, and outlier removal on data variability and error rates. Results indicated that replicate sampling alone was insufficient to control variability; however, combining optimized processing techniques, particularly angle selection and outlier exclusion, substantially improved data reliability and precision. Outlier removal, along with selecting maximum angle values, contributed to smoother growth curves with less variability, enhancing the robustness of the data. The reliability of these phenotypic traits was further validated through genome- wide association studies (GWAS), using 12,127 SNPs iden- tified via a deep learning-based GBS(Genotyping-by-Sequen-cing) pipeline. The GWAS analysis identified significant SNPs associated with rice height on chromosomes 3, 6, and 9. Notably, genes such as OsBIG, OsGH9B3, OsSTRL2, and OsCCS52A were confirmed to play roles in rice height, linking them to growth hormone biosynthesis pathways. This optimized phenotypic analysis method demonstrates strong potential for identifying trait-associated markers in rice and other crop plants.
Abstract
In this study, we developed and validated an optimized phenotypic analysis method using time-series data collected throughout the full growth cycle of 96 rice cultivars. Height growth curves were compared across three phenotyping tools (ImageJ, OpenCV, and PlantCV), each of which showed distinct performance characteristics at different stages of rice growth. ImageJ displayed significant variability in early growth stages, while OpenCV suffered from decreased accuracy during later stages. PlantCV, however, provided stable and consistent results across all stages, making it the most reliable tool for this phenotypic analysis. We also examined the effects of replicate sampling, camera angle selection, and outlier removal on data variability and error rates. Results indicated that replicate sampling alone was insufficient to control variability; however, combining optimized processing techniques, particularly angle selection and outlier exclusion, substantially improved data reliability and precision. Outlier removal, along with selecting maximum angle values, contributed to smoother growth curves with less variability, enhancing the robustness of the data. The reliability of these phenotypic traits was further validated through genome- wide association studies (GWAS), using 12,127 SNPs iden- tified via a deep learning-based GBS(Genotyping-by-Sequen-cing) pipeline. The GWAS analysis identified significant SNPs associated with rice height on chromosomes 3, 6, and 9. Notably, genes such as OsBIG, OsGH9B3, OsSTRL2, and OsCCS52A were confirmed to play roles in rice height, linking them to growth hormone biosynthesis pathways. This optimized phenotypic analysis method demonstrates strong potential for identifying trait-associated markers in rice and other crop plants.
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