An application of image processing techniques for detection of diseases on brinjal leaves using k-means clustering method

R. Anand, S. Veni, J. Aravinth
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引用次数: 97

Abstract

This work presents a method for identifying plant leaf disease and an approach for careful detection of diseases. The goal of proposed work is to diagnose the disease of brinjal leaf using image processing and artificial neural techniques. The diseases on the brinjal are critical issue which makes the sharp decrease in the production of brinjal. The study of interest is the leaf rather than whole brinjal plant because about 85-95 % of diseases occurred on the brinjal leaf like, Bacterial Wilt, Cercospora Leaf Spot, Tobacco mosaic virus (TMV). The methodology to detect brinjal leaf disease in this work includes K-means clustering algorithm for segmentation and Neural-network for classification. The proposed detection model based artiifical neural networks are very effective in recognizing leaf diseases.
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基于k均值聚类方法的图像处理技术在茄子叶片病害检测中的应用
本工作提出了一种鉴定植物叶片病害的方法和一种仔细检测病害的方法。本研究的目的是利用图像处理和人工神经技术对茄子叶片疾病进行诊断。茄子病害是导致茄子产量急剧下降的关键问题。由于茄子中85- 95%的病害发生在叶片上,如青枯病、斑孢病、烟草花叶病毒(TMV)等,因此对茄子的研究重点是叶片而不是整株。本研究采用K-means聚类算法分割和神经网络分类两种方法检测茄子叶片病害。提出的基于人工神经网络的检测模型对叶片病害的识别非常有效。
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