[论文解读] Image-Based Classification of Olive Species Specific to Turkiye with Deep Neural Networks

İrfan Atabaş, Hatice Karatas|arXiv (Cornell University)|Feb 26, 2026

Smart Agriculture and AI被引用 0

一句话总结

该研究使用立体成像数据和迁移学习的卷积神经网络（MobileNetV2、EfficientNetB0）来对土耳其的五种橄榄树种进行分类，其中EfficientNetB0达到94.5%的准确率。

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.

研究动机与目标

• 推动对土耳其本地橄榄树种的自动识别。
• 开发基于图像的深度学习物种分类管线。
• 评估多种CNN架构以确定表现最佳的模型。
• 评估迁移学习在本农业成像任务中的有效性。

提出的方法

• 使用立体相机捕捉五种橄榄树种的图像。
• 对图像进行预处理，确保适合CNN分析。
• 在迁移学习下训练并比较MobileNetV2和EfficientNetB0架构。
• 在保留的测试集上评估模型以确定准确率。
• 基于准确性和稳健性确定表现最佳的模型。

实验结果

研究问题

• RQ1 深度学习模型是否能够利用图像数据准确分类土耳其的五种橄榄树种？
• RQ2 哪种CNN架构（MobileNetV2 vs EfficientNetB0）在该任务上具有更高的分类准确率？
• RQ3 在测试集上最佳模型的达到的准确率是多少？
• RQ4 迁移学习是否提升了这一橄榄树种分类问题的性能？

主要发现

• EfficientNetB0在所测试的模型中取得最高准确率，为94.5%。
• 对该任务均使用了迁移学习的MobileNetV2和EfficientNetB0。
• 使用立体成像以获取适合CNN分类的数据。
• 预处理步骤确保了图像适合深度学习分析。