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

arXiv: https://arxiv.org/abs/2603.00168 · HTML: https://mlsanity.com/paper/2603.00168/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [An Enhanced YOLOv8 Model for Real-Time and Accurate Pothole Detection and Measurement](https://mlsanity.com/paper/2505.04207/) (2025)
- [Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines](https://mlsanity.com/paper/2312.01062/) (2023)
- [A Novel Public Dataset for Strawberry (Fragaria x ananassa) Ripeness Detection and Comparative Evaluation of YOLO-Based Models](https://mlsanity.com/paper/2602.15656/) (2026)

## Abstract (arXiv)

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.