探索深度学习方法用于核桃现象型品种分类
1Faculty of Engineering and Natural Sciences, Department of Software Engineering, Konya Technical University, Konya, Türkiye.
International journal of food science
|March 26, 2025
概括
深度学习模型使用图像分析准确地分类核桃品种. 将深度学习特征提取与后勤回归相结合,实现了农产品分类的最高成功率.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 农产品的有效分类对于质量评估和供应链管理至关重要.
- 核桃分类由于品种差异和需要准确的质量分级而带来了挑战.
研究的目的:
- 分析深度学习和数据科学方法来分类核桃品种.
- 评估不同深度学习架构和机器学习分类器的性能,用于核桃图像分类.
主要方法:
- 收集了来自钱德勒,费尔诺,霍华德和奥古兹拉品种的核桃图像数据集.
- 进行了两项实验: 1) 使用深度学习模型 (InceptionV3,VGG-19,VGG-16) 作为直接分类器, 2) 使用深度学习进行特征提取,然后使用支持向量机 (SVM),后勤回归 (LR) 和k-最近邻居 (k-NN) 分类器.
主要成果:
- 在第一个实验中,InceptionV3获得了最高的分类准确度,超过了VGG-19和VGG-16.
- 第二个实验,利用深度学习来提取特征,显示了整体成功率的提高.
- 结合InceptionV3用于特征提取和物流回归用于分类,产生了最高的成功率.
结论:
- 深度学习方法对于基于视觉数据的快速准确的农产品分类非常有效.
- 这些发现表明,通过先进的计算技术,有可能增强农业分类系统.
- InceptionV3和物流回归模型为自动核桃质量评估提供了一个有希望的方法.
关键词:
开始V3 开始V3在SVM中,SVM是SVM.在VGG-16中.在VGG-19中,VGG-19在VGG-19中使用.这是分类分类的分类.深度学习是一种深度学习.k-NNN 在线观看逻辑回归的逻辑回归胡果和胡果是一个很好的选择.更多相关视频
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