植物-CNN-ViT:植物分类与卷积神经网络和视觉转换器组合
Chin Poo Lee1, Kian Ming Lim1, Yu Xuan Song1
1Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.
Plants (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种新的Plant-CNN-ViT组合模型,用于准确的植物叶子分类. 通过组合四个预训练模型,它克服了数据限制,在多个数据集上实现了近乎完美的准确性.
科学领域:
- 植物学 植物学
- 计算机科学 计算机科学
- 机器学习 机器学习
背景情况:
- 植物叶子的分类对于物种识别至关重要.
- 机器学习模型提高了准确性,但需要大量的训练数据.
- 数据稀缺对许多植物物种构成重大挑战.
研究的目的:
- 开发一个准确和高效的植物叶子分类模型.
- 克服依赖数据的机器学习模型的局限性.
- 通过结合多种深度学习架构来利用集体学习.
主要方法:
- 提出了一个Plant-CNN-ViT组合模型,集成视觉变压器,ResNet-50,DenseNet-201和Xception.
- 视觉转换器使用自我注意力来聚焦功能.
- ResNet-50,DenseNet-201和Xception使用剩余,密集和可分离的卷积来有效地提取特征.
主要成果:
- 在Flavia和Folio Leaf数据集上实现了100.00%的准确性.
- 在瑞典Leaf数据集上获得了100.00%的准确性,在马来西亚Kew Leaf数据集上获得了99.83%.
- 证明了组合方法在植物叶子分类中的有效性.
结论:
- 植物-CNN-ViT组合模型显著提高了植物叶子分类的准确性.
- 该模型有效地解决了有限的培训数据的挑战.
- 这种方法为植物物种识别提供了可靠的解决方案.
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