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Plant Disease Detection Using SVM

Saddamhusen Shaikh, Rajesh K. Agrawal



Diseases in plants are responsible for major production and economic losses and also reduction in yield. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself, it detects the symptoms of diseases. Detection on plant is very critical for defensible agriculture. It is very challenging to monitor the plant diseases physically. It requires tremendous amount of work, expertize in the plant diseases, and also requires the excessive processing time. Hence, image processing is used for the detection of plant diseases. The system involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper discussed the methods used for the detection of plant diseases using their leaves images. In this system, K-means clustering algorithm, Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrix (GLCM) are used for disease detection. This method is simulated in MATLAB environment and is examined on diseased images of grapes calculating their texture features. 

Keywords: Segmentation, features extraction, discrete wavelet transform (DWT), gray level co-occurrence matrix (GLCM), K-means clustering.

Cite this Article

Shaikh Saddamhushen J, Agrawal Rajesh K. Plant Disease Detection Using SVM. Journal of Image Processing & Pattern Recognition Progress. 2017; 4(3): 8–12p.


Segmentation, Features extraction, Discrete Wavelet Transform (DWT), Gray Level Co-occurrence Matrix (GLCM, K-means clustering.

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