Classification of pistachios with deep learning and assessing the effect of various datasets on accuracy

Pistachio is a healthy and delicious snack with high economic value, especially when product consists of only open pistachios. For this reason, many studies have been carried out in the literature to classify Pistachio according to whether they are open or closed. In this study, the classification process of pistachios was carried out according to whether they are open or closed using deep learning techniques. The prominent aspect of the study is that the datasets obtained with an industrial experimental set-up are used in the training of the network in order to be suitable for industrial applications and to classify it with high accuracy. In this study, AlexNet and Inception V3 structure were trained and tested with this industrial data set, the test accuracy was calculated as 96.13% and 96.54%, respectively. In order to compare the industrial data set and the desktop data set, both data sets were created. As a result of training and testing the AlexNet structure with this desktop data set, the test accuracy was calculated as 100%. When the test images from industrial dataset are fed to the network structure trained with the desktop dataset, the test accuracy was obtained as 61.75%. On the contrary, when desktop data set is fed to Alexnet structure trained with industrial data set, test accuracy is calculated as 99.84%. This clearly demonstrates how accurately the industrial dataset performs in industrial classification applications, while the desktop dataset has poor accuracy in industrial applications.