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.
