HybNet:用于药用植物物种识别的混合深度模型
B R Pushpa1, S Jyothsna1, S Lasya1
1Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, India.
MethodsX
|January 20, 2025
概括
这项研究引入了三种混合深度学习模型,用于准确检测植物物种,这对医学和保护至关重要. 混合模型3通过增强具有Squeeze和Excitation层的功能实现了94.24%的准确性.
科学领域:
- 计算机科学 计算机科学
- 植物学 植物学
背景情况:
- 实时识别植物物种对于医学和生物多样性至关重要.
- 挑战包括不受约束的环境,尺度变化和复杂的叶子结构.
研究的目的:
- 开发和评估新的混合深度学习模型,以准确识别植物物种.
- 以实时,不受约束的条件来解决当前方法的局限性.
主要方法:
- 开发了三种混合模型,将卷积神经网络 (CNN) 合并用于特征提取.
- 模型包括VGG16/MobileNet+KNN,MobileNet/ResNet50+深度学习分类器和MobileNetV2+挤压和激发 (SE) 层.
- 实验是在实时条件下对自己创建的药用植物数据集进行实验.
主要成果:
- 模型1 (VGG16/MobileNet+KNN) 实现了85.85%的准确性.
- 模型2 (MobileNet/ResNet50+深度学习分类器) 实现了88%的准确性.
- 模型3 (MobileNetV2+SE层) 以94.24%的准确度表现出卓越的性能,这是由于功能增强和重新校准.
结论:
- 混合深度学习模型显著提高了植物物种检测准确度.
- 功能增强技术,特别是SE层,对于高性能至关重要.
- 开发的模型显示了植物学和保护领域实时应用的潜力,尽管使用的数据集很小.
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