在联合网络框架中验证了草药植物的本地化和药理识别
Misaj Sharafudeen1, Vinod Chandra S S1, Aswathy A L1
1Machine Intelligence Research Laboratory, Department of Computer Science, University of Kerala, India.
Computers in biology and medicine
|April 13, 2024
概括
这项研究引入了以人为中心的人工智能系统,用于使用叶子分析准确识别药用植物. 人工智能模型提高了精度,并提供了药物鉴定信息,帮助收集者和专家.
科学领域:
- 药理学是指药理学,即药理学是指药理学.
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 准确识别药用植物对于防止错误物种的不良影响至关重要.
- 传统的植物识别方法可能耗时且容易出现错误.
研究的目的:
- 开发一个强大的以人为中心的人工智能系统,用于准确的药用植物识别.
- 将叶子定位,分类和药理学信息检索集成到一个应用程序中.
主要方法:
- 基于YOLOv7的叶子定位器和基于DenseNet的叶子类验证器被组合在一起.
- 为了可靠性和稳定性,使用了信心评分分析仪和组合技术 (多数投票).
- 纳入了文字和音频药理学数据的自动检索.
主要成果:
- 综合系统的精度达到了97.33%,比单独的YOLO模型 (77.45%) 显著改善.
- 合奏技术在各种条件下表现出一致的灵敏度和稳定性.
- 该系统为已识别的植物提供实时药理识别信息.
结论:
- 开发的AI系统为药用植物识别提供了可靠和高效的解决方案.
- 智能手机应用程序为植物收集者和专家提供了有价值的实时数据.
- 这种方法提高了药理识别领域的安全性和实用性.
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