背景:小麦(TriticumaestivumL.)是世界上重要的粮食作物,盐碱胁迫严重影响其生长发育,尤其是在苗期。因此,盐碱胁迫下小麦幼苗的无损检测可以为小麦育种提供更全面的技术支持,种植和管理。
结果:这项研究的重点是使用融合技术对小麦幼苗中盐碱胁迫的水分信号预测和分类。在收集和分析小麦幼苗的横向弛豫时间和多光谱成像(MSI)信息后,使用四个回归模型来预测水分信号。K-最近邻(KNN)和高斯-朴素贝叶斯(GNB)模型结合五次交叉验证对小麦幼苗胁迫的预测进行分类。结果表明,小麦幼苗通过一定的机制增加了结合水含量,从而增强了盐碱胁迫。在相同的Na浓度下,碱胁迫对水分的影响,小麦幼苗的生长和光谱强于盐胁迫。梯度提升决策回归树模型在预测小麦水分信号方面表现最好,决定系数(R2P)为0.98,均方根误差为109.60。它还具有较短的训练时间(1.48s)和有效的预测速度(1300obs/s)。KNN和GNB在对融合数据集进行分类时表现出显著增强的预测性能,与单独使用单个数据集相比。特别是,GNB模型在融合的数据集上表现最好,精度,回想一下,准确性,F1评分为90.30,88.89%,88.90%,和0.90。
结论:在相同的Na浓度下,碱胁迫对含水量的影响,光谱,小麦的生长强于盐胁迫,这更不利于小麦的生长。低场核磁共振与MSI技术的融合可以提高小麦胁迫的分类,为盐碱胁迫下小麦幼苗的快速、准确监测提供了有效的技术方法。
BACKGROUND: Wheat (Triticum aestivum L.) is an important grain crops in the world, and its growth and development in different stages is seriously affected by saline-alkali stress, especially in seedling stage. Therefore, nondestructive detection of wheat seedlings under saline-alkali stress can provide more comprehensive technical support for wheat breeding, cultivation and management.
RESULTS: This research focused on moisture signal prediction and classification of saline-alkali stress in wheat seedlings using fusion techniques. After collecting and analyzing transverse relaxation time and Multispectral imaging (MSI) information of wheat seedlings, four regression models were used to predict the moisture signal. K-Nearest Neighbor (KNN) and Gaussian-Naïve Bayes (GNB) models were combined with fivefold cross validation to classify the prediction of wheat seedling stress. The results showed that wheat seedlings would increase the bound water content through a certain mechanism to enhance their saline-alkali stress. Under the same Na concentration, the effect of alkali stress on moisture, growth and spectrum of wheat seedlings is stronger than salt stress. The Gradient Boosting Decision Regression Tree model performs the best in predicting wheat moisture signals, with a coefficient of determination (R2P) of 0.98 and a root mean square error of 109.60. It also had a short training time (1.48 s) and an efficient prediction speed (1300 obs/s). The KNN and GNB demonstrated significantly enhanced predictive performance when classifying the fused dataset, compared to using single datasets individually. In particular, the GNB model performing best on the fused dataset, with Precision, Recall, Accuracy, and F1-score of 90.30, 88.89%, 88.90%, and 0.90, respectively.
CONCLUSIONS: Under the same Na concentration, the effects of alkali stress on water content, spectrum, and growth of wheat were stronger than that of salt stress, which was more unfavorable to the growth of wheat. The fusion of low-field nuclear magnetic resonance and MSI technology can improve the classification of wheat stress, and provide an effective technical method for rapid and accurate monitoring of wheat seedlings under saline-alkali stress.