Image-Based Classification of Olive Varieties Native to Turkiye Using Multiple Deep Learning Architectures: Analysis of Performance, Complexity, and Generalization

Hatice Karatas, Irfan Atabas
2/20/2026
cs.CV

No confident code implementation found for this paper.

Abstract

This study compares multiple deep learning architectures for the automated, image-based classification of five locally cultivated black table olive varieties in Turkey: Gemlik, Ayvalik, Uslu, Erkence, and Celebi. Using a dataset of 2500 images, ten architectures - MobileNetV2, EfficientNetB0, EfficientNetV2-S, ResNet50, ResNet101, DenseNet121, InceptionV3, ConvNeXt-Tiny, ViT-B16, and Swin-T - were trained using transfer learning. Model performance was evaluated using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Cohen's Kappa, ROC-AUC, number of parameters, FLOPs, inference time, and generalization gap. EfficientNetV2-S achieved the highest classification accuracy (95.8%), while EfficientNetB0 provided the best trade-off between accuracy and computational complexity. Overall, the results indicate that under limited data conditions, parametric efficiency plays a more critical role than model depth alone.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

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

92% match2026

OliVaR: Improving Olive Variety Recognition using Deep Neural Networks

87% match2023

Training a Custom CNN on Five Heterogeneous Image Datasets

82% match2026

Performance Analysis of Image Classification on Bangladeshi Datasets

80% match2026

Detecting Olives with Synthetic or Real Data? Olive the Above

79% match2023

Automated Tomato Maturity Estimation Using an Optimized Residual Model with Pruning and Quantization Techniques

79% match2025

Cite this paper

@article{karatas2026imagebased,
  title  = {Image-Based Classification of Olive Varieties Native to Turkiye Using Multiple Deep Learning Architectures: Analysis of Performance, Complexity, and Generalization},
  author = {Hatice Karatas and Irfan Atabas},
  year   = {2026},
  eprint = {2602.18530},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2602.18530v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2602.18530.svg)](https://codeofpaper.com/paper/2602.18530)

Discussion