Image-Based Classification of Olive Species Specific to Turkiye with Deep Neural Networks
This research provides an effective solution for the automatic identification and quality control of olive species, which could benefit agricultural producers and quality control processes.
This study classified five local olive species from Turkiye using deep neural networks on images captured with a stereo camera. The EfficientNetB0 model achieved an optimal accuracy of 94.5% for this classification task.
In this study, image processing and deep learning methodologies were employed to automatically classify local olive species cultivated in Turkiye. A stereo camera was utilized to capture images of five distinct olive species, which were then preprocessed to ensure their suitability for analysis. Convolutional Neural Network (CNN) architectures, specifically MobileNetV2 and EfficientNetB0, were employed for image classification. These models were optimized through a transfer learning approach. The training and testing results indicated that the EfficientNetB0 model exhibited the optimal performance, with an accuracy of 94.5%. The findings demonstrate that deep learning-based systems offer an effective solution for classifying olive species with high accuracy. The developed method has significant potential for application in areas such as automatic identification and quality control of agricultural products.