Nondestructive Prediction of Rice Seed Viability Using Spectral and Spatial Information Modeling of Visible–Near Infrared Hyperspectral Images

Highlights An NIR-Vis hyperspectral imaging approach was developed to predict the viability of rice seeds. Through multi-step accelerated aging, seed lots in various states were used for the experiments. Models using spectral information and spectral-spatial information of hyperspectral images were used and compared. Abstract. Rice is one of the world’s most important food crops, and rice seed viability is an important factor in rice crop production. In this study, a visible–near infrared (vis–NIR) hyperspectral imaging system and spectral–spatial information modeling are used to predict the viability of rice seeds. Experimental samples are prepared using seeds harvested in two different years and artificially aged for various periods. Vis-NIR hyperspectral acquisition and germination tests of the prepared seed samples are performed. Partial least square (PLS)–discriminant analysis, a support vector machine (SVM), a PLS–SVM, a PLS–artificial neural network, and a one-dimensional–convolutional neural network (CNN) for the mean spectra of seeds, as well as a CNN, a PLS–CNN, and dual branch networks for the hyperspectral images of the seeds are applied for viability prediction modeling. Result shows that an accuracy of approximately 90% and high f1 scores can be obtained in most models. Furthermore, it is confirmed that models using spectral and spatial information can classify hard samples more effectively. Keywords: Deep learning, Hyperspectral images, Rice, Seed, Spectroscopy, Viability.