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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In this study, firmness classification potential of tomato fruits was investigated by using colour parameters measured with a colour measuring device. 202 'Bandita F1' greenhouse tomatoes were used as trial material. In damage free colour measurements carried out by Minolta CR-400 colour measurement device, L*, a* and b* colour parameters were considered as main parameters. Other colour parameters (a*xb*, a*2, b*2 and a*/b*) were derived from main colour parameters. These colour parameters were associated with tomato firmness. In tomato firmness measurements, the force value at the skin rupture point was used and this value was expressed as tomato firmness. Tomato samples were grouped according to firmness by using clustering analysis method. In addition, linear discrimination analysis method was used in the classification of tomatoes according to firmness. Classification accuracy was improved by linear discrimination analysis and the number of parameters used was decreased with stepwise regression analysis method. The association between tomato firmness and colour parameters (L*, a*, b*, a*xb*, a*2, b*2 and a*/b*) was determined with Pearson Correlation test. Statistical analysis results showed that the association between tomato firmness and colour parameters was significant (P<0.01). According to linear discrimination analysis results, linear classification accuracy was calculated as 85.64% for main colour parameters approach and as 90.59% for seven colour parameters approach. The results of linear discrimination analysis performed by using the most important three colour parameters determined with stepwise regression analysis method showed that correct classification accuracy of tomatoes was 89.10%. The results showed that firmness classification of tomatoes could be done by using colour parameters and linear discrimination analysis method. Domates meyvelerinin renk parametrelerine göre sertlik sınıflandırmasıKeywords: Tomato firmness Maturity stage Colour parameters Clustering analysis Linear discrimination analysis ÖZET Bu çalışmada, renk ölçüm cihazı ile ölçülen renk parametreleri kullanılarak domates meyvelerinin sertlik sınıflandırma potansiyeli araştırılmıştır. Deneme materyali olarak 202 adet 'Bandita F1' sera domatesleri kullanılmıştır. Minolta CR-400 model renk ölçüm cihazı kullanılarak yapılan hasarsız renk ölçümlerinde, L*, a* ve b* renk parametreleri ana parametreler olarak dikkate alınmıştır. Diğer renk parametreleri (a*xb*, a*2, b*2 ve a*/b*) ana renk parametrelerinden türetilmiştir. Bu renk parametreleri, domates sertliği ile ilişkilendirilmiştir. Domates sertliği ölçümlerinde, kabuk yırtılma noktasındaki kuvvet değeri kullanılmış ve bu değer domates sertliği olarak ifade edilmiştir. Kümeleme analiz yöntemi kullanılarak domates örnekleri sertliğine göre gruplandırılmıştır. Ayrıca, domateslerin sertliğine göre sınıflandırma işlemlerinde, doğrusal ayırma analiz yöntemi kullanılmıştır. Sınıflandırma hassasiyeti doğrusal ayırma analizi ile iyileştirilmiş ve kullanılan parametre sayısı stepwise regr...

Cashew is one of the major commercial commodities contributing to the national economy of Tanzania as foreign revenue. And yet still the processing of cashew is run locally using manual labour for a big part. If processed well under ideal conditions, cashews kernels are expected to be white in colour. But due to various factors like prolonged roasting in the steam chambers or over-drying, some cashew kernels tend to have a slight brown colour, and these are referred to as scorched cashews. Despite sharing the same characteristics with white cashew kernels, including nutritional quality, these cashew kernels are supposed to be graded differently. In many places around the world, particularly in Tanzania, the sorting and grading process of cashew kernels is performed by hand. In international trade, cashew grading is very important and this means more effective and consistent methods need to be applied in this stage of production in order to increase the quality of the products. The objective of this study was to evaluate the use of traditional Machine Learning techniques in the classification of cashew kernels as white or scorched by using colour features. In this experiment, various colour features were extracted from the images. The extracted features include the means (μ), standard deviations (σ), and skewness (γ) of the channels in RGB and HSV colour spaces. The relevant features for this classification problem were selected by applying the wrapper approach using the Boruta Library in Python, and the irrelevant ones were removed. 5 models are studied and their efficiencies analysed. The studied models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbour. The Decision Tree model recorded the least accuracy of 98.4%. The maximum accuracy of 99.8% was obtained in the Random Forest model with 100 trees. Due to simplicity in application and high accuracy, the Random Forest is recommended as the best model from this study.

The aim of this study is to find out the factors affecting mechanical bruising of peach and to develop a model for the prediction of the bruise that occurs in peach in harvest and post‐harvest processes. For this purpose, experiments were carried out with Redhaven, Glohaven, and Dixired peach varieties. The peaches were harvested in three different periods. Researches on experiment materials were carried out after storing in two different environments, room temperature and cold storage conditions. In the study, pendulum impact test setup was used to create different degrees of bruise on peaches. Regression models were created with “Multiple Linear Regression” analysis to prediction the amount of bruise volume by using impact energy, maximum contact force, radius of curvature, and Magness–Taylor force factors obtained based on the measurement and calculation results. As a result, it was found that all of the multiple linear regression models created for the prediction of bruising in peach fruit were statistically significant (p ≤.01). Practical Applications Damages in fruits cause quality loss in the product. Mechanical damage can occur at many different stages, such as during the harvesting and postharvest processing of the material, or during manual handling. Peach fruit is sensitive to different types of damage that may occur during and after harvest. In this study, models were developed for the prediction of fruit damage by working on some peach cultivars. With the study, suggestions were made for the prevention of damage to the peach during harvest and postharvest processes. In addition, in the light of the information obtained, it will provide a reference for the improvement of systems and designs used or to be installed for harvest and postharvest processing of peach fruit.