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小麦黄病的分类是基于纹理和深度特征的结合

Tolga Hayıt¹, Hasan Erbay^2,3, Fatih Varçın⁴

¹Department of Computer Engineering, Faculty of Engineering and Architecture, Yozgat Bozok University, Yozgat, 66900 Türkiye.

Multimedia tools and applications

|June 26, 2023

概括

早期检测小麦黄对于作物管理至关重要. 结合纹理和深度学习功能,特别是使用CNN-CGLCM_HSV模型与SVM,在识别疾病严重程度方面实现了92.4%的准确性.

科学领域:

农业科学农业科学
计算机视觉计算机视觉
植物病理学植物病理学

背景情况:

黄严重影响全球小麦产量和质量.
早期和准确的疾病严重程度检测对于有效的作物管理至关重要.
传统的纹理分析方法正在通过深度学习方法得到增强.

研究的目的:

调查结合纹理和深度特征的可行性,用于在小麦中检测黄的严重程度.
为了评估不同的颜色空间 (RGB,HSV,L*a*b) 和分类方法 (SVM,KNN).
为准确的黄色生严重性评估开发一个强大的模型.

主要方法:

使用灰色水平共发生矩阵 (GLCM) 提取的纹理特征,结合了颜色信息.
利用了预先训练的卷积神经网络 (CNN) 的深度特征,特别是DenseNet.
开发和比较黄色-19数据集上的各种分类模型.
使用不同颜色空间的支持向量机 (SVM) 和K-最近邻近 (KNN) 分类器.

主要成果:

结合模型CNN-CGLCM_HSV,利用HSV颜色空间和SVM分类器,实现了最高准确率92.4%.
纹理和深度特征的整合显著改善了疾病严重程度分类的性能.
不同的颜色空间和分类器在捕捉与疾病相关的模式方面表现出不同的有效性.

结论:

将基于GLCM的纹理特征与CNN的深度特征相结合,为黄色生严重性检测提供了一种强大的方法.
该CNN-CGLCM_HSV模型显示在小麦疾病管理中具有很高的实际应用潜力.
进一步的研究可以探索先进的功能融合技术和更大的数据集,以提高准确性.

关键词:

深度功能功能功能功能深度功能在DenseNet中,使用的是DenseNet.在 KNN KNN 标签上.在SVM中,SVM是SVM.纹理特征纹理特征黄色的生是黄色的生.

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