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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.
Alper Taner; Yeşim Öztekin; Hüseyin Duran. Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut. Sustainability 2021, 13, 6527 .
AMA StyleAlper Taner, Yeşim Öztekin, Hüseyin Duran. Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut. Sustainability. 2021; 13 (12):6527.
Chicago/Turabian StyleAlper Taner; Yeşim Öztekin; Hüseyin Duran. 2021. "Performance Analysis of Deep Learning CNN Models for Variety Classification in Hazelnut." Sustainability 13, no. 12: 6527.
Bruising of fresh fruits during harvest and postharvest processing is a major problem in the horticultural industry. Damage is largely a result of impact, particularly during sorting, grading and packing operations. In this study, fruit-to-surface impact on commercial packing lines were simulated in the laboratory by individually dropping fresh ‘Glohaven’, ‘J.H. Hale’ and ‘Loring’ peaches onto 3 impact surfaces from a range of heights (10–120 mm). A specially constructed impact-testing device with a 500-mm pendulum arm was used to drop fruit onto an uncovered steel impact surface or the same surface covered in either the poron or rubber foam typically used by packing houses as cushioning surfaces. After the impact testing of fruit was completed, the procedure was repeated using an electronic fruit (IRD), which recorded peak impact acceleration and velocity changes. Bruising threshold values for the different peach varieties were determined by evaluating the relationship between peak impact acceleration and velocity changes of the IRD and the areas of bruising of the fruit. The experimental data was used to calculate the drop height required to produce a bruising area of 100 mm2 for each of the three peach varieties and impact surfaces. The findings showed poron to be a more appropriate material for surface padding than rubber foam.
Yeşim Benal Öztekin; Büşra Güngör. Determining impact bruising thresholds of peaches using electronic fruit. Scientia Horticulturae 2019, 262, 109046 .
AMA StyleYeşim Benal Öztekin, Büşra Güngör. Determining impact bruising thresholds of peaches using electronic fruit. Scientia Horticulturae. 2019; 262 ():109046.
Chicago/Turabian StyleYeşim Benal Öztekin; Büşra Güngör. 2019. "Determining impact bruising thresholds of peaches using electronic fruit." Scientia Horticulturae 262, no. : 109046.
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.
Alper Taner; Yeşim Benal Öztekin; Ali Tekgüler; Hüseyin Sauk; Hüseyin Duran. Classification of Varieties of Grain Species by Artificial Neural Networks. Agronomy 2018, 8, 123 .
AMA StyleAlper Taner, Yeşim Benal Öztekin, Ali Tekgüler, Hüseyin Sauk, Hüseyin Duran. Classification of Varieties of Grain Species by Artificial Neural Networks. Agronomy. 2018; 8 (7):123.
Chicago/Turabian StyleAlper Taner; Yeşim Benal Öztekin; Ali Tekgüler; Hüseyin Sauk; Hüseyin Duran. 2018. "Classification of Varieties of Grain Species by Artificial Neural Networks." Agronomy 8, no. 7: 123.
In this study, drying characteristics of bay laurel (Laurus nobilis L.) fruits wereinvestigated in a laboratory scale hot-air dryer at air temperature in a range of 70 to 100°C. Moisture transfer from the test samples was described by applying the Fick’s diffusion model and the effective diffusivity was calculated. Temperature dependence of the effective diffusivity was described by the Arrhenius-type relationship. The experimental drying data of bay laurel fruits were used to fit page, logarithmic, Two-term and approximation of diffusion, and Midilli et al. (2002) models and the statistical validity of models tested were determined by non-linear regression analysis. The Two-term model showed a better fit to experimental drying data obtained when compared with other models. Key words: Bay laurel fruits, convective air drying, moisture ratio, effective diffusivity, activation energy.
Y. Benal Yurtlu. Drying characteristics of bay laurel (Laurus nobilis L.) fruits in a convective hot-air dryer. African Journal of Biotechnology 2011, 10, 9593 -9599.
AMA StyleY. Benal Yurtlu. Drying characteristics of bay laurel (Laurus nobilis L.) fruits in a convective hot-air dryer. African Journal of Biotechnology. 2011; 10 (47):9593-9599.
Chicago/Turabian StyleY. Benal Yurtlu. 2011. "Drying characteristics of bay laurel (Laurus nobilis L.) fruits in a convective hot-air dryer." African Journal of Biotechnology 10, no. 47: 9593-9599.