Image Processing and Classification, A Method for Plant Disease Detecion
Simranjeet kaur1, Geetanjali Babbar2, Gagandeep3
1Simranjeet kaur, M. Tech, Chandigarh Engineering College, Landran (Maholi), India.
2Geetanjali Babbar, Assistant Professor, Chandigarh Engineering College, Landran (Maholi), India.
3Dr. Gagandeep, Department of Computer & Science and Engineering, Chandigarh Engineering College (CEC), Landran (Maholi), India.
Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 868-871 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11390789S19/19©BEIESP | DOI: 10.35940/ijitee.I1139.0789S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The plant disease detection is the major issue of the computer vision and machine learning. The plant disease detection has the various phases like pre-processing, segmentation, feature extraction and classification. In the existing technique support vector machine is used for the classification. The support vector machine approach has the low accuracy for the plant disease detection and also it can classify data into two classes which affect its performance. The proposed methodology is based on the region based segmentation, textual feature analysis and k-nearest neighbor method is applied for the classification. The proposed method is implemented in MATLAB and results are analyzed in terms of accuracy. The proposed technique has high accuracy and compared to existing technique.
Keywords: Plant disease detection, GLCM, K-mean, KNN
Scope of the Article: Classification