Authors

Abstract

Physiological index data and low-field nuclear magnetic resonance (LF-NMR) spectral data of rice seed samples from three varieties harvested in different years were collected through a combination of the standard germination test and an LF-NMR test. Three parameters of seed vigor: germination energy, germination percentage, and germination index, were calculated based on the physiological index data of the rice seed samples to determine their vigor over the years after harvest. LF-NMR Carr-Purcell-Meiboom-Gill (CPMG) sequence echo-peak data were used as the input, and rice seed vigor was used as the output to establish discriminative models using principal component analysis, support vector machine, logistic regression, K-nearest neighbor, artificial neural network, and Fisher’s linear discriminant. The results showed that models constructed using any algorithm, except for principal components analysis-algorithm distinguished between seeds with high and low vigor, while models constructed using Fisher’s linear discriminant algorithm gave the best results. This study provided a rapid, accurate, and non-destructive method to test rice seed vigor, offering theoretical support and a reference for rice seed-sorting and storage research.

Abstract in Chinese

采用标准发芽试验和低频核磁共振(LF-NMR)相结合的方法，采集了3个不同年份水稻种子的生理指标数据和低场核磁共振波普数据。根据水稻种子生理指标数据，计算发芽势、发芽率和发芽指数3个参数，对不同收获年份的水稻种子进行活力高低的区分。将LF-NMR 硬脉冲序列回波峰点数据作为输入，水稻种子活力水平作为输出，建立结合主成分分析、支持向量机、逻辑回归、k近邻、人工神经网络和Fisher线性判别法建立判别模型。结果表明，除主成分分析算法外，任何一种算法所构造的模型都能区分种子的活力高低，而Fisher线性判别算法所构造的模型效果最好。本研究为水稻种子活力的快速、准确、无损检测提供了一种方法，为水稻分选贮藏研究提供了理论支持和参考。