Assessment of maize seed vigor under saline-alkali and drought stress based on low field nuclear magnetic resonance

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 (T₂₁), semi-bound water (T₂₂), and free water (T₂₃), in addition parabolic water (T₂₄). The Pearson correlation analysis was performed with T₂ 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 T₂ 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 T₂ 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.

Plant phenotyping relevance

低磁場NMRによる種子の水分状態測定とニューラルネットワークによる活力・発育状態予測が研究の中心であり、植物状態の取得・推定手法として実質的に評価されている。

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