用深度学习自动识别土耳其五种本地橄榄品种，准确率达94.5%。
Image-Based Classification of Olive Species Specific to Turkiye with Deep Neural Networks
- 采用MobileNetV2与EfficientNetB0进行迁移学习图像分类。
- EfficientNetB0模型准确率最高，达94.5%。
- 适合农业自动化识别与质量控制场景。
本研究采用图像处理与深度学习方法，实现对土耳其本地栽培的五种橄榄品种的自动分类。使用双目相机采集图像并预处理以适配分析。对比了MobileNetV2与EfficientNetB0两种卷积神经网络架构，均通过迁移学习优化。训练与测试结果显示，EfficientNetB0模型表现最优，准确率达到94.5%。结果表明，基于深度学习的系统可高效、精准地完成橄榄品种分类，具有在农产品自动识别与质量控制中的应用潜力。
原文摘要 · Abstract (English)
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.
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