Li Jianguo, Zhao Liyan, Fan Hongzeng, Zhao Falin, He Dandan, Li Bo, Wang Jibin, Xie Guosheng, Hu Zhen, Fan Chuchuan, Wang Lingqiang
State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning, China.
College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China.
BMC Plant Biol. 2025 Mar 27;25(1):386. doi: 10.1186/s12870-025-06396-y.
Plant stem structural characteristics are crucial factors determining plant lodging resistance, while high throughput methods for rapid surveys of these traits are still lacking in sorghum.
Among 103 sorghum accessions, two kinds of stem powders (dry and water-washed) were subject to visible and near-infrared spectra acquisition, and 16 models (combinations) for stem structural characteristics were generated, revealing that the support vector machine regression model has significant positive effects on the prediction of stem structural characteristics while powder type and pretreatment of spectra has minor effects on the prediction of stem structural characteristics. In addition, we found that stem structure characteristics were positively correlated with agronomic traits but negatively correlated with lodging index which is the criterion that negatively accounts for plant lodging resistance.
This study for the first time provided a precise and high throughput method for the prediction of sorghum stem structural characteristics based on spectra, which could facilitate the improvement of lodging resistance in crop breeding.
植物茎结构特征是决定植物抗倒伏性的关键因素,而高粱中仍缺乏用于快速测量这些性状的高通量方法。
在103份高粱种质中,对两种茎粉(干茎粉和水洗茎粉)进行了可见光谱和近红外光谱采集,并建立了16种茎结构特征模型(组合),结果表明支持向量机回归模型对茎结构特征的预测具有显著的正向影响,而粉的类型和光谱预处理对茎结构特征的预测影响较小。此外,我们发现茎结构特征与农艺性状呈正相关,但与倒伏指数呈负相关,倒伏指数是衡量植物抗倒伏性的负面指标。
本研究首次提供了一种基于光谱的精确且高通量的高粱茎结构特征预测方法,这有助于在作物育种中提高抗倒伏性。