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Diagnosis of diseases on cotton leaves using principal component analysis classifier

机译：主要成分分析分类器诊断棉花叶片疾病

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This paper addresses the problem of diagnosis of diseases on cotton leaf using Principle Component Analysis (PCA), Nearest Neighbourhood Classifier (KNN). Cotton leaf data analysis aims to study the diseases pattern which are defined as any deterioration of normal physiological functions of plants, producing characteristic symptoms in terms of undesirable color changes mainly occurs upon leaves; caused by a pathogen, which may be any agent or deficiencies. The predictions of diseases on cotton leaves by human assistance may be wrong in some cases. Using machine vision techniques, it is possible to increase scope for detection of various diseases within visible as well invisible wavelength regions. After implementing PCA/KNN multi-variable techniques, it is possible to analyse the statistical data related to the Green (G) channel of RGB image. Green channel is taken into consideration for faithful feature collection since disease or deficiencies of elements are reflected well by green channel. In most of the cases diseases are seen on the leaves of the cotton plant such as Blight, Leaf Nacrosis, Gray Mildew, Alternaria, and Magnesium Deficiency. The classification accuracy of PCA/KNN based classifier observed is 95%.

机译：本文通过原理分析（PCA），最近的邻域分类器（KNN）解决了棉花薄膜疾病诊断问题。棉叶数据分析旨在研究疾病模式，该疾病模式定义为植物正常生理功能的任何恶化，在不希望的颜色变化方面产生特征症状，主要发生在叶子上;由病原体引起的，这可能是任何药剂或缺陷。在某些情况下，人类援助对棉花叶片的疾病预测可能是错误的。使用机器视觉技术，可以增加视觉中可见的各种疾病的范围，以及不可见波长区域。在实现PCA / KNN多变量技术之后，可以分析与RGB图像的绿色（G）通道相关的统计数据。由于绿色通道反映了绿色渠道，因此考虑到忠实的特征收集，以考虑到忠实的特征。在大多数情况下，在棉花植物的叶片上看到疾病，如枯萎病，叶状病症，灰色霉菌，alertaria和镁缺乏。观察到的PCA / KNN的分类器的分类精度为95％。

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《Annual IEEE India Conference》|2014年||共5页
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作者
Gulhane V.A.; Kolekar M.H.;
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中图分类无线电电子学、电信技术;
关键词
computer vision; cotton; data analysis; image colour analysis; pattern classification; plant diseases; principal component analysis; KNN; PCA; RGB image; cotton leaf data analysis; disease diagnosis; machine vision technique; nearest neighbourhood classifier; principal component analysis; Cotton; Covariance matrices; Diseases; Feature extraction; Image color analysis; Principal component analysis; Vectors; Cotton Leaves; Diagnosis; Nearest Neighbourhood Classifier; Principal Component Analysis;

机译：计算机视觉;数据分析;图像颜色分析;植物疾病;植物疾病;主成分分析;KNN;PCA;RGB图像;棉叶数据分析;疾病诊断;机器视觉技术;主成分分析;棉协方差矩阵;疾病;特征提取;图像颜色分析;主成分分析;载体;棉花;诊断;最近的邻域分类器;主成分分析;

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Diagnosis of diseases on cotton leaves using principal component analysis classifier

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