药用植物叶病的分类使用最佳加权特征与扩展的适应性DenseNet和注意力机制
1KPR Institute of Engineering and Technology, Arasur, Coimbatore, Tamil Nadu, 641407, India. leelavathi.rajan@gmail.com.
Scientific reports
|October 22, 2025
概括
这项研究引入了一个深度学习框架,用于识别药用植物叶病. 这种新的方法实现了90.69%的准确性,提高了农业疾病预测.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 生物技术是生物技术.
背景情况:
- 农业部门的经济重要性受到疾病识别预测错误的阻碍.
- 现有的药用植物疾病分类方法存在局限性.
研究的目的:
- 开发一个深度学习框架,准确分类药用植物叶病.
- 提高农业疾病识别准确度.
主要方法:
- 使用过和对比度有限的自适应式直方图形平衡 (CLAHE) 的图像预处理.
- 通过自适应值进行叶片细分.
- 使用定制的多尺度VGG16架构进行深度特征提取.
- 使用混合斑马与羊群优化 (HZKHO) 算法进行特征选择.
- 使用基于注意力的扩展适应性密度网 (A-DADensenet) 模型进行分类.
主要成果:
- 拟议的框架实现了疾病药用植物叶子的分类准确率为90.69%.
- 深度学习与混合优化的整合显著提高了分类性能.
结论:
- 开发的深度学习框架在识别患病的药用植物叶子方面表现出高的有效性.
- 该方法显示了现实世界农业应用的巨大潜力,提高了疾病预测的准确性.
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