Hong Joon Ki, Baek Jeongho, Kim Jae Young, Kim Song Lim, Lyu Jae Il, Kang Sang-Ho, Song Jiseon, Kim Nyunhee, An Eunsook, Lee Hyun-Sook, Kim Kyung-Hwan, Chung Yong Suk, Mansoor Sheikh
National Institute of Agricultural Sciences, Rural Development Administration, 370 Nongsaengmyeong-ro, Jeonju 54874, Republic of Korea.
National Institute of Agricultural Sciences, Rural Development Administration, 370 Nongsaengmyeong-ro, Jeonju 54874, Republic of Korea.
J Plant Physiol. 2025 Aug;311:154544. doi: 10.1016/j.jplph.2025.154544. Epub 2025 Jun 6.
This study utilized plant phenomics image analysis technology to explore the agronomic characteristics of rice cultivars, aiming to enhance growth stability, yield potential, and digital data for rice breeding. RGB images were captured at three lateral angles during the growth period of the plants using ScanLyzer, LemnaTec. A total of 42 agronomic traits were analyzed across 102 rice cultivars, categorized into three maturing groups. In addition, to evaluate the measurement accuracy, 9 phenotypic traits, the panicle length (Pl), panicle count (Pc), and number of seeds were also measured destructively after harvest. Parameter estimated revealed that the Pl trait exerted the strongest positive effect on seed production across all groups analyzed, with coefficients (β) of 0.459 for the entire population, 0.456 in the early-maturing group, 0.537 in the medium-maturing group, and 0.574 in the medium-late maturing group (p < 0.05). Other traits, such as maximum area (Am), and maximum height (Hm), also positively influenced seed production but to a lesser extent. Notably, duration of maximum value of rice plant width had a significant negative effect in the early-maturing group (β = -0.369, p < 0.05). Correlation analyses revealed strong positive relationships between seed production and various traits across maturity classes, notably with days to maximum height, Pl, Pc, and seed count. Additionally, panicle length and count emerged as pivotal factors influencing seed numbers. These findings underscore the varying impacts of agronomic traits on seed yield depending on cultivars and maturity groups, offering valuable insights for the selection of rice cultivars aimed at optimizing seed production.
本研究利用植物表型组学图像分析技术探索水稻品种的农艺性状，旨在提高水稻生长稳定性、产量潜力以及为水稻育种提供数字数据。在植株生长期间，使用LemnaTec公司的ScanLyzer在三个侧角拍摄RGB图像。对102个水稻品种的42个农艺性状进行了分析，这些品种分为三个成熟组。此外，为了评估测量准确性，在收获后还对9个表型性状进行了破坏性测量，包括穗长（Pl）、穗数（Pc）和种子数。参数估计显示，在所有分析的组中，Pl性状对种子产量的正向影响最强，整个群体的系数（β）为0.459，早熟组为0.456，中熟组为0.537，中晚熟组为0.574（p < 0.05）。其他性状，如最大面积（Am）和最大高度（Hm），也对种子产量有正向影响，但程度较小。值得注意的是，水稻植株宽度最大值持续时间在早熟组有显著负向影响（β = -0.369，p < 0.05）。相关性分析表明，不同成熟类别的种子产量与各种性状之间存在强正相关关系，特别是与达到最大高度的天数、Pl、Pc和种子数。此外，穗长和穗数是影响种子数量的关键因素。这些发现强调了农艺性状对种子产量的影响因品种和成熟组而异，为旨在优化种子生产的水稻品种选择提供了有价值的见解。
