  Pistachio nut is mainly consumed as raw, salted or roasted because of its
high nutritional properties and favorable taste. Pistachio nuts with shell and
kernel defects, besides not being acceptable for a consumer, are also prone to
insects damage, mold decay, and aflatoxin contamination. In this research, a
deep learning-based imaging algorithm was developed to improve the sorting of
nuts with shell and kernel defects that indicate the risk of aflatoxin
contamination, such as dark stains, oily stains, adhering hull, fungal decay
and Aspergillus molds. This paper presents an unsupervised learning method to
classify defective and unpleasant pistachios based on deep Auto-encoder neural
networks. The testing of the designed neural network on a validation dataset
showed that nuts having dark stain, oily stain or adhering hull with an
accuracy of 80.3% can be distinguished from normal nuts. Due to the limited
memory available in the HPC of university, the results are reasonable and
justifiable.
