In some countries, most hazelnuts are cracked using semi-industrial or hand-crafted machines and marketed as open-shell. In the process of hazelnut cracking, because of the different sizes and shapes of hazelnuts, many hazelnuts leave the cracking machine in the form of cracked or closed-shell. The presence of cracked or closed-shell hazelnuts reduces the marketability of the product. Therefore, after the cracking operation, the separation of cracked or closed-shell from whole hazelnuts has largely been conducted by visual inspection, which is time-consuming, labor-intensive, and lacks of accuracy. So, the purpose of this study was to use the Deep Convolutional Neural Network (DCNN) algorithm to classify the hazelnuts into two classes of open-shell and closed-shell or cracked hazelnuts. To compare the proposed method with pre-trained DCNN models, three models including ResNet-50, Inception-V3, and VGG-19 were investigated. The results of the proposed model (accuracy 98 % and F1˗Score 96.8) showed that the proposed DCNN has good capability in predicting hazelnut classes. Compared with pre-trained models, because of the small size and simple architecture of the proposed model, this model can be a good substitute for complex and large model such as Inception-V3. Overall, the results indicate that crack on the hazelnut surface can be successfully detected automatically, and the proposed DCNN has a high potential to facilitate the development of hazelnut sorter based on surface crack.
