基于图像的土耳其本土橄榄品种分类：使用多种深度学习架构的性能、复杂性和泛化分析
Image-Based Classification of Olive Varieties Native to Turkiye Using Multiple Deep Learning Architectures: Analysis of Performance, Complexity, and Generalization
AI总结：
本研究使用多种深度学习架构对土耳其本土橄榄品种进行图像分类，发现EfficientNetV2-S在准确率上表现最佳，而EfficientNetB0在准确率与计算复杂性之间达到最佳平衡。
摘要：
本研究比较了多种深度学习架构，用于自动化分类土耳其本地种植的五种黑表橄榄品种：Gemlik、Ayvalik、Uslu、Erkence和Celebi。使用2500张图像的数据集，通过迁移学习训练了十种架构：MobileNetV2、EfficientNetB0、EfficientNetV2-S、ResNet50、ResNet101、DenseNet121、InceptionV3、ConvNeXt-Tiny、ViT-B16和Swin-T。模型性能通过准确率、精确率、召回率、F1分数、马修相关系数（MCC）、科恩Kappa、ROC-AUC、参数数量、FLOPs、推理时间以及泛化差距进行评估。EfficientNetV2-S实现了最高的分类准确率（95.8%），而EfficientNetB0在准确率和计算复杂性之间提供了最佳的权衡。总体而言，结果表明，在数据有限的条件下，参数效率比模型深度本身更为关键。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
