In this study, an Artificial Neural Network (ANN) model was developed in order to classify varieties belonging to grain species. Varieties of bread wheat, durum wheat, barley, oat and triticale were utilized. 11 physical properties of grains were determined for these varieties as follows: thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters. It was found that these properties had been statistically significant for the varieties. An Artificial Neural Network was developed for classifying varieties. The structure of the ANN model developed was designed to have 11 inputs, 2 hidden and 2 output layers. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour were used as input parameters; and species and varieties as output parameters. While classifying the varieties by the ANN model developed, R2, RMSE and mean error were found to be 0.99, 0.000624 and 0.009%, respectively. In classifying the species, these values were found to be 0.99, 0.000184 and 0.001%, respectively. It has shown that all the results obtained from the ANN model had been in accordance with the real data.

In evaluating agricultural products, knowing the specific product varieties is important for the producer, the industrialist, and the consumer. Human labor is widely used in the classification of varieties. It is generally performed by visual examination of each sample by experts, which is very laborious and time-consuming with poor sensitivity. There is a need in commercial hazelnut production for a rapid, non-destructive and reliable variety classification in order to obtain quality nuts from the orchard to the consumer. In this study, a convolutional neural network, which is one of the deep learning methods, was preferred due to its success in computer vision. A total of 17 widely grown hazelnut varieties were classified. The proposed model was evaluated by comparing with pre-trained models. Accuracy, precision, recall, and F1-Score evaluation metrics were used to determine the performance of classifiers. It was found that the proposed model showed a better performance than pre-trained models in terms of performance evaluation criteria. The proposed model was found to produce 98.63% accuracy in the test set, including 510 images. This result has shown that the proposed model can be used practically in the classification of hazelnut varieties.

The purpose of this research was to determine the drying characteristics of Uzun variety pistachios in a convectional dryer with air velocity of 1 m/s at 50, 55, 60, and 65°C. Through the end of drying, moisture transfer from in‐hull, and in‐shell pistachio samples was defined by using the diffusion model of Fick. The Arrhenius‐type relationship was used to establish the temperature dependence of the effective moisture diffusivity. The activation energy of in‐hull and in‐shell pistachios was determined to be 56.04 and 34.18 kJ/mol, respectively. Five well‐known models, that is, the logarithmic, page, two‐term, Midilli et al., and approximation of diffusion models were fit to data of moisture ratio. The Midilli et al., and the two‐term models were chosen to accurately describe the hot‐air drying behavior of in‐hull and shell pistachio samples. As shelf‐life criteria, the water activity (aw) values of fresh and dried pistachios were determined at different ambient air temperatures. The enthalpy (ΔH) and entropy (ΔS) values of the pistachio samples decreased with increasing the air temperature, while Gibbs‐free energy (ΔG) increased as the air temperature increased. Novelty impact statement Pistachio kernel has rich nutritional significance for the human diet due to its 50%–60% fat content and unsaturated fatty acid and, the drying is the most important post‐harvest process in the pistachio industry. Pistachio kernels must be dried below aw of 0.82 and 0.70 for short‐ and long‐term storage and to avoid fungal development, respectively. This work evaluates the drying conditions and presents a mathematical model for hot‐air drying of pistachio nuts and, the results show that drying of pistachio can be carried out in industrial convective dryers.

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