Assessment of maize seed vigor under saline-alkali and drought stress based on low field nuclear magnetic resonance. (August 2022) - Song, Ping; Yue, Xia; Gu, Ying; Yang, Tao
Abstract : To detect maize seed vigor under salt-alkaline and drought stress conditions, transverse relaxation time, physiological index, and electron microscopy images of germinating seeds were studied under different stress conditions. The results showed that the water in germinating maize seeds exist in bound water ( T 21 ), semi-bound water ( T 22 ), and free water ( T 23 ), in addition parabolic water ( T 24 ). The Pearson correlation analysis was performed with T 2 relaxation parameters and seed vigor parameters, and this analysis yield nine optimized parameters. To predict vigor levels under different stress conditions, an error backpropagation artificial neural network model was developed, wherein the T 2 chirality parameter was used as the input value, and the seed germination indices of different stress levels were used as the output values. The model could predict the environmental stress level of maize seed growth. The optimized parameter set showed a prediction accuracy of 92.50%, thus outperforming the T 2 relaxation information model without parameter optimization (75.01%). The proposed method can collect data during the germination of maize seeds without any interference and achieve large-scale prediction of seed development status by small sample collection. With the stress environment was aggravated, the physiological structure of seed cells was changed, and the cell structure was destroyed and the ability of water absorption was disappeared. This analysis provides theoretical support and a reference basis for maize planting and production. Graphical abstract: Image 1 Highlights: Error backpropagation artificial neural network model (BP-ANN) to predict the vigor level of corn seeds. The germination of corn seeds is severely affected by salt-alkaline and drought stress. Nuclear magnetic data of a few corn seeds samples are used to predict the large-scale seed development.