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 this study, working postures during the manual harvesting of hazelnuts were analyzed with four different ergonomic risk assessment methods, namely REBA, AWBA, OWAS, and RULA, and the differences between them were revealed. The most appropriate method for manual harvesting activities was determined. Hand harvesting of hazelnuts; It consists of three main workstations: shaking the branches by hand, collecting them manually from the branches, and containing them manually from the ground. There are three different ways of sitting in the hand-harvesting of hazelnuts: standing on knees, bending over, or cross-legged. During the manual harvest, the workers' movements at all workstations were observed and recorded with a camera. Images were analyzed using the ErgoFellow 2.0 program. As a result of the analysis, working postures that may cause musculoskeletal disorders (MSD) were determined. According to the analysis results, shaking the hazelnut by hand and harvesting from the branch by hand were risky compared to the other three analysis methods, except for the Ovako Working Posture Analyzing System (OWAS) method. In manual harvesting from the ground, high-risk scores were obtained in all working postures, Rapid Entire Body Assessment (REBA), Agricultural Whole Body Assessment (AWBA), Rapid Upper Limb Assessment (RULA), and OWAS methods, and it was determined in the category of urgent action change. Among the ergonomic risk analysis methods, it can be said that AWBA is the analysis method that includes the closest positions of working postures in the manual harvest of hazelnuts

In this study, the possibilities of using the prototype manufactured hazelnut harvesting machine with a mechanical efficient harvesting unit and hazelnut harvesting machine with a pneumatic efficient harvesting unit in the mechanical harvesting of hazelnuts grown in flat and near flat land conditions were examined and the nut losses and the efficiency values of separating foreign materials were determined to reveal how these machines affect the hazelnut harvesting system. For this purpose, the trials were carried out under five different orchard yield conditions (71.74, 143.48, 215.23, 286.97, and 358.72 kg ha-1). As a result of the experiments, the nut loss and foreign material removal efficiency obtained by hazelnut harvesting with a hazelnut harvesting machine with a mechanical effective harvesting unit were determined between 34.39-37.92% and 96.91-95.62%, respectively. The nut losses and foreign material removal efficiency values obtained by hazelnut harvesting machine with a pneumatic effective harvesting unit were determined between 6.84-5.07% and 93.33-86.73%, respectively. The data to be obtained as a result of the study, in addition to examining the mechanical harvesting of hazelnuts, will enable the reasons for the changes that may occur in the performance characteristics of existing machines to be explained and suggestions can be made for improvement.