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.

Having the advantages of speed, suitability and high accuracy, computer vision has been effectively utilized as a non-destructive approach to automatically recognize and classify fruits and vegetables, to meet the increased demand for food quality-sensing devices. Primarily, this study focused on classifying apple varieties using machine learning techniques. Firstly, to discern how different convolutional neural network (CNN) architectures handle different apple varieties, transfer learning approaches, using popular seven CNN architectures (VGG16, VGG19, InceptionV3, MobileNet, Xception, ResNet150V2 and DenseNet201), were adopted, taking advantage of the pre-trained models, and it was found that DenseNet201 had the highest (97.48%) classification accuracy. Secondly, using the DenseNet201, deep features were extracted and traditional Machine Learning (ML) models: support vector machine (SVM), multi-layer perceptron (MLP), random forest classifier (RFC) and K-nearest neighbor (KNN) were trained. It was observed that the classification accuracies were significantly improved and the best classification performance of 98.28% was obtained using SVM algorithms. Finally, the effect of dimensionality reduction in classification performance, deep features, principal component analysis (PCA) and ML models was investigated. MLP achieved an accuracy of 99.77%, outperforming SVM (99.08%), RFC (99.54%) and KNN (91.63%). Based on the performance measurement values obtained, our study achieved success in classifying apple varieties. Further investigation is needed to broaden the scope and usability of this technique, for an increased number of varieties, by increasing the size of the training data and the number of apple 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.

It is critically necessary to maximize the efficiency of agricultural methods while concurrently reducing the cost of production. Varieties, types, and fruit classification grades are crucial to fruit production. High expenditure, inconsistent subjectivity, and tedious labor characterize traditional and manual varieties classification. This study developed machine learning (ML) models to classify ten apple varieties, extracting the histogram of oriented gradient (HOG) and color moments from RGB apple images. Support vector machine (SVM), random forest classifier (RFC), multilayer perceptron (MLP), and K-nearest neighbor (KNN) classification models were trained with 10-fold stratified cross-validation (Skfold) by using the textural and color features, and a GridSearch was implemented to fine-tune the hyperparameters. The trained models, SVM, RFC, MLP, and KNN were tested with separate test data and performed well, having an accuracy of 98.17%, 96.67%, 98.62%, and 91.28%, respectively. Having the top results, the MLP and SVM models demonstrated the potential of applying HOG and color moments to train ML models for classifying apple varieties. This study suggests conducting further research to thoroughly examine additional image features and determine the impact of combining features and utilizing different classifiers.

Hazelnut, which has the most common cultivation area after almonds and walnuts among the shell fruits in the world, contains high levels of fat, protein, carbohydrates, various minerals and vitamins. Hazelnut has a broad growing area around the world, and Turkey accounts for 58% of world production with 776,000 tons annually. With this production capacity, Turkey ranks first in hazelnut production over the world. Harmful insects in hazelnut trees are the main factors that reduce hazelnut yield and quality. Pesticides are sprayed with backpack sprayers in the fight against these pests in hazelnut trees. Farmers are directly exposed to pesticides in these practices, which use high amounts of pesticides and water. In recent years, the use of unmanned agricultural vehicles in agriculture has increased. Drones are also used in pesticide applications in agriculture. In this study, the suitability of pesticide applications with the drone in hazelnut fields in Giresun province in terms of field conditions and spraying efficiency was investigated. In September 2021, applications were made with DJI Agras MG-1P model spraying drone in a selected hazelnut orchard in Uzgur village of Giresun province. The drone has 4 Teejet XR11001VS fan jet nozzles. Water-sensitive papers were placed on different regions on the hazelnut trees to be sprayed for drop measurements. In the experiments, hazelnut trees were sprayed using water at different heights and spraying rates. 6 flights were carried out at 1.5 and 2 meters altitudes and 1, 2 and 3 L.da-1 spray rates, and three hazel trees were sprayed as three repetitions in each flight. After the flight trials, the water-sensitive papers were scanned on the scanner, and the volume median diameters and the number of droplets per square centimetre were calculated in the DepositScan software. As a result of the analyses done, it was observed that the applications performed at 1 L.da-1 and 2 L.da-1 spray rates would not be sufficient in terms of spraying efficiency. As the drone flight altitude increased, the accumulation of the drops on the inner leaves decreased. In terms of drop distribution, the most homogeneous application parameter was found to be 1.5 meters high above the upper leaves of the hazel trees and 3 L.da-1 spray rate.

The purpose of this study was to investigate the possibility of solar-powered thermoelectric elements (Peltier) to harvest water from the atmosphere. An Arduino-controlled model capable of harvesting water from air was designed and used in laboratory experiments. The developed prototype system was analyzed by using a package program using computational fluid dynamics. During the experiments, temperature and humidity data were recorded on a computer. We found that the Peltier used in the system developed temperature and relative humidity differences between the inlet air and outlet air from the system. These differences in temperature and humidity created an environment for harvesting water by condensing the humidity in air.