使用转移学习对长和桑托里萨进行分类
Agus Pratondo1, Elfahmi Elfahmi2, Astri Novianty3
1Department of Multimedia Engineering, School of Applied Sciences, Telkom University, Bandung, West Java, Indonesia.
PeerJ. Computer science
|June 22, 2023
概括
这项研究开发了一个手机图像模型来区分黄 (Curcuma longa) 和temulawak (Curcuma zanthorrhiza). 人工智能模型实现了高准确度,有助于识别这些有价值的津比拉系草药.
科学领域:
- 植物学和药物鉴定学
- 计算机科学和人工智能 人工智能
- 农业技术 农业技术
背景情况:
- 黄 (Curcuma longa) 和泰穆拉瓦克 (Curcuma zanthorrhiza) 是不同的津比拉属植物,具有不同的营养特征和经济价值.
- 准确识别这些根茎对公众来说是具有挑战性的,需要技术解决方案.
- 这两种物种都含有宝贵的化合物,如黄素和精油,突出了它们的重要性.
研究的目的:
- 开发一种自动化系统,使用手机图像来区分Curcuma longa和Curcuma zanthorrhiza.
- 为了利用深度学习技术来准确地分类Zingiberaceae根茎.
- 为识别黄和泰穆拉瓦克提供一个实用的工具,支持它们的经济和药用应用.
主要方法:
- 使用手机摄像头获得Curcuma longa和Curcuma zanthorrhiza根茎的图像.
- 使用预训练的VGG-19和Inception V3模型与ImageNet权重实现转移学习.
- 深度学习模型的培训和验证,用于根茎分类的精选图像数据集.
主要成果:
- 开发的模型显示了很高的分类准确性,VGG-19达到92.43%,Inception V3达到94.29%.
- 该研究成功地创建了一个基于图像的黄和temulawak的功能性分类系统.
- 结果表明,开发的技术有可能在实践中得到广泛应用.
结论:
- 使用深度学习的基于手机的图像分类是区分黄和temulawak的有效方法.
- 高准确率表明这些模型可用于实际的识别任务.
- 这项技术可以提高这些重要的草药资源的利用和贸易.
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