Identification of harvesting year of barley seeds using near-infrared hyperspectral imaging combined with convolutional neural network

Abstract

To evaluate the quality and safety of the seeds, identification of the harvesting year is one of the main parameters as the quality of the seeds is deteriorated during storage due to seed aging. In this study, hyperspectral imaging in the near-infrared range of 900-1700 nm was used to non-destructively identify the harvesting time of the barley seeds. The seeds samples including three years from 2017 to 2019 were collected. An end-to-end convolutional neural network (CNN) model was developed using the mean spectra extracted from the ventral and dorsal sides of the seeds. CNN model outperformed other classification models (K-nearest neighbors and support vector machines with and without spectral preprocessing) with a test accuracy of 97.25%. This indicated that near-infrared hyperspectral imaging combined with CNN could be used to rapidly and non-destructively identify the harvesting year of the barley seeds.