Proposing Classification Technique for Plant Disease Detection in Image Processin
Manish Jha1, Tarun Gulati2, Vikas Mittal3
1Manish Jha, Department of ECE, MMEC, MMDU, Mullana, Ambala Haryana, India.
2Tarun Gulati, Professor, Department of ECE, MMEC, MMDU, Mullana, Ambala, Haryana, India.
3Vikas Mittal, Associate Professor, Department of ECE, MMEC, MMDU, Mullana, Ambala, Haryana, India.
Manuscript received on 08 June 2019 | Revised Manuscript received on 13 June 2019 | Manuscript Published on 08 July 2019 | PP: 180-183 | Volume-8 Issue-8S3 June 2019 | Retrieval Number: H10440688S319/19©BEIESP
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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 technique used for the processing of digital data obtained from pictures is identified as image processing. Plants and crops are ruining because of the excessive use of fertilizers and insecticides. The experts observe the plant disease with their naked eye and identify and detect the type of diseases plant is suffering from. In order to identify infections from input pictures, plant disease detection approach is implemented. An image processing approach is implemented in this research study. This approach is relied on the extraction of textural feature, segmentation and classification. The textural features are extracted from the picture with the help of GLCM algorithm. The input picture is segmented with the help of k-mean clustering algorithm. For classification, theKNN classification is used in this research. This leads to improve accuracy of detection and also leads to classify data into multiple classes. The results of the proposed algorithm are analyzed in terms of various parameters accuracy, precision, recall and execution time. The accuracy of proposed algorithm is increased upto 10 to 15 percent.
Keywords: GLCM , K-mean, KNN, SVM
Scope of the Article: Classification