CISCS:基于类间相似性的药用植物物种群的分类,使用机器学习
N Shobha Rani1, Bhavya K R2, I Jeena Jacob2
1MURTI Research Centre, Smart Agriculture Lab, Department of Artificial Intelligence and Data Science, GITAM School of Technology, Bengaluru, GITAM (Deemed to be) University, India.
MethodsX
|October 27, 2025
概括
一个新的多层次特征融合模型准确地分类视觉上相似的印度药用植物. 这种方法克服了深度学习的局限性,为植物识别提供了强大的解决方案,并支持生物多样性研究.
科学领域:
- 植物学和药物识别学
- 计算机科学和机器学习
- 生物信息学是一种生物信息学.
背景情况:
- 药用植物的可靠分类对于医疗保健的质量和安全至关重要.
- 现有的方法与视觉上相似的物种和不平衡的数据集作斗争.
- 像ResNet18和VGG16这样的深度学习模型显示出由于过拟合而导致的局限性.
研究的目的:
- 开发一个强大的,计算效率高的模型来对印度药用植物物种进行分类.
- 为了应对高跨类视觉相似性和数据集不平衡的挑战.
- 提高植物物种识别的准确性和可靠性.
主要方法:
- 一个新的多级特征融合模型,结合了3D规范化颜色直方图,扩展均局部二进制模式 (LBP),加博过器和面向梯度直方图 (HOG).
- 基于SMOTE的合成增强来解决阶级不平衡.
- 机器学习分类器的软投票组合,具有用于分类的共弦相似度指标.
主要成果:
- 拟议的模型在印度药用植物数据集的第一组中达到100%的准确性,在第三组中达到95.82%的准确性.
- 持续优于深度学习基线,在其他组中准确度超过90%.
- 在高度类间相似性和数据集不平衡的条件下表现出稳健性.
结论:
- 多层次特征融合模型为药用植物分类提供了对深度学习的优越替代方案.
- 这种方法在计算上是高效和可扩展的,支持生物多样性和生态研究.
- 这种方法提高了植物识别的可靠性,这对于制药和保护工作至关重要.
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