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.
Abstract (translated)
在这项研究中，使用了图像处理和深度学习的方法来自动分类在土耳其种植的地方橄榄品种。立体相机被用来拍摄五种不同的橄榄品种的图片，然后对这些图片进行预处理以确保它们适合分析。为了图像分类，采用了卷积神经网络（CNN）架构，具体来说是MobileNetV2和EfficientNetB0模型，并通过迁移学习的方法来优化这些模型。 训练和测试结果表明，EfficientNetB0模型表现出最佳性能，其准确率为94.5%。研究发现证明了基于深度学习的系统能够以高精度有效地对橄榄品种进行分类。开发出的方法在自动识别和农产品质量控制等领域具有重要的应用潜力。
URL
https://arxiv.org/abs/2603.00168
