一种轻量级的深度学习方法,用于使用特征融合进行药用叶子图像分类
Vinay Gautam1, Gaganpreet Kaur1, G S Pradeep Ghantasala2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Scientific reports
|October 2, 2025
概括
这项研究引入了一种新的联合学习模型,用于准确识别药用植物的叶子. 计算机视觉系统达到98.90%的准确性,帮助研究人员和农民.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 植物学 植物学
背景情况:
- 准确识别药用植物叶子对于它们的营养特性至关重要,但对人类观察者来说具有挑战性.
- 需要自动化系统来帮助研究人员和农民有效和准确地识别叶子.
研究的目的:
- 开发一种新的基于联合学习的特征融合深度学习模型,用于对药用植物叶子进行分类.
- 通过混合方法来增强功能集成和分类准确性.
主要方法:
- 使用的邻近组件分析-卷积神经网络 (NCA-CNN) 框架用于特征集成.
- 从RGB图像中提取混合特征 (手工制作的LBP,HOG和深度特征).
- 使用正统相关性分析 (NCA) 融合特征,并使用CNN分类器进行分类.
主要成果:
- 拟议的模型在测试数据集上实现了98.90%的异常准确度.
- 在处理多种分辨率的各种图像特征方面表现出强大的性能.
- 成功培训和评估使用联合学习的客户端模型.
结论:
- 基于学习的新型联合特征融合模型显示了药用植物叶子分类的卓越性能.
- 这种方法在促进学术研究和农业应用方面具有重大潜力.
- 强调了整合手工制作和深度特征的有效性,以提高准确性.
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