基于实体学视觉特征的RESNET-50药用植物分类
Sapna Renukaradhya1,2, Sheshappa Shagathur Narayanappa3, Pravinth Raja2
1Sir M. Visvesvaraya Institute of Technology, affiliated to Visvesvaraya Technological University, Belagavi.
Network (Bristol, England)
|March 3, 2025
概括
准确的植物物种识别对生物多样性至关重要. 这项研究结合了群体智能,机器学习 (GRNN) 和深度学习 (RESNET50) 以实现自动化叶子分类,达到99.9%的准确性.
科学领域:
- 植物学 植物学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 手动识别植物物种是耗时和劳动密集的.
- 技术进步需要有效的自动化方法来识别植物.
- 自动化植物物种识别面临重大挑战.
研究的目的:
- 开发一个自动化的植物叶子分类系统.
- 为了利用叶子特征和类之间的本体关系.
- 使用混合人工智能技术来提高识别准确度.
主要方法:
- 使用本体关系进行叶子分类.
- 集群智能算法 (粒子集群,子搜索) 用于特征识别.
- 机器学习 (一般回归神经网络 - GRNN) 和深度学习 (RESNET50) 用于分类.
- 结合GRNN和RESNET50的结果,使用关联规则.
主要成果:
- GRNN模型的准确度达到了98.8%.
- RESNET50模型的准确性达到99%.
- 组合模型 (GRNN + RESNET50) 在15种药用叶类型中实现了99.9%的识别准确度.
结论:
- 拟议的混合人工智能模型显著提高了植物叶子识别准确度.
- 本体关系与先进的人工智能技术相结合,为自动化物种识别提供了强大的解决方案.
- 这种方法为生物多样性研究和植物管理提供了非常有效的工具.
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