Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning

Abstract

Viability and vigor of rice seeds are related to the yield. The existing seed viability and vigor detection methods cannot meet the demand for precise planting, and a method that can quickly and non-destructively predict the vigor of rice seeds is needed. In this study, near-infrared hyperspectral imaging was used to determine the viability and vigor of naturally-aged rice seeds. Standard germination test was conducted to determine the reference values of the viability and vigor. Convolutional neural network (CNN) and conventional machine learning methods (support vector machine (SVM) and logistic regression (LR)) were built using full range spectra and characteristic wavelengths selected by principal component analysis (PCA) to predict the viability and vigor of different varieties of rice seeds under natural aging conditions. The overall results showed that deep learning methods and conventional machine learning methods could predict the viability and vigor of different varieties of rice seeds well, and the accuracy of most models was over 85%. Models using full spectra and the characteristic wavelengths showed close results. Models on all varieties performed closely to those on single variety. This study provided an effective method for fast, non-destructive and efficient prediction of rice seed viability and vigor.