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.

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.

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.

Corn is one of the major crops in Sudan. Disease outbreaks could significantly reduce maize production, causing huge damage. Conventionally, disease diagnosis is made through visual inspection of the damage in the fields or through laboratory tests conducted by experts on the affected plant parts of the crop, but this process typically requires highly skilled personnel and can be time-consuming to complete the necessary tasks in order to fast and accurately detect disease and reduce the risks of crop failure due to diseases outbreak, machine-learning methods can be implemented. This study aims to use traditional machine learning techniques to detect Mize disease using image preprocessing techniques. A total of 600 images were obtained from the open-source Plant Village dataset for experimentation. In this study, image segmentation was done using K-Means clustering, and from the images, a total of 4 GLCM texture features and 2 statistical features were extracted. In this study, four traditional machine learning algorithms were applied to detect diseased maize leaves (Common Rust and Gray Spot Leaf), and healthy maize leaves. The results showed that all the algorithms performed well in identifying the diseased and healthy leaves, with accuracy rates ranging from 90% to 92.7%. The highest accuracy scores were obtained by Support Vector Machine and Artificial Neural Networks, respectively.

Background: Structural and behavioral factors are among the causes of occupational accidents in agriculture. The SACURIMA Cost network developed a questionnaire to measure the determinants of farmers’ safety behavior based on the extended theory of planned behavior (TPB). Extended TPB adds subjective norms, subjective control, physical barriers and safety culture to TPB. Objective: The aim of this study is to test the psychometric properties of the “Farmers Safety Behaviors questionnaire” developed by the SACURIMA Cost network. Methods: A Turkish version of the questionnaire was applied to 305 farmers producing six different agricultural products in Turkey. The tool consists of 64 items measuring the determinants of four risk-related behaviors (fall prevention, machine handling, chemical-pesticide use, and animal handling) in a single extended TPB model. Results: The alpha values for the six dimensions ranged from 0.69 to 0.89. The confirmatory factor analysis results for all dimensions were at acceptable levels (CFI range = 0.93–0.99; RMSEA range = 0.03–0.09). Four path models were used to test the behaviors and their predictors, and the results were found to be predictive. The criterion and known groups’ validity analyses results were sufficient. Conclusion: The “Farmers Safety Behaviors Questionnaire” is a valid and reliable tool to measure the determinants of occupational safety behaviors in Turkish farmers.