Image-Based Classification of Olive Species Specific to Turkiye with Deep Neural Networks

TL;DR

This paper presents a deep learning approach using CNNs, specifically EfficientNetB0, to classify olive species in Turkiye from images with high accuracy, aiding agricultural identification and quality control.

Contribution

The study introduces an effective deep learning-based system for olive species classification using stereo images and transfer learning, achieving 94.5% accuracy.

Findings

EfficientNetB0 achieved 94.5% accuracy.

Deep learning models are effective for olive classification.

Transfer learning improved model performance.

Abstract

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.