An Approach of Tomato Leaf Disease Detection Based on SVM Classifier
J. Keerthi1, Suman Maloji2, P. Gopi Krishna3
1J Keerthi, Department of ECM, Koneru Lakshmaiah Education Foundation, Guntur, (Andhra Pradesh), India.
2Dr. M Suman, Professor, Department of ECM, Koneru Lakshmaiah Education Foundation, Guntur, (Andhra Pradesh), India.
3P Gopi Krishna, Assistant Professor, , Department of ECM, Koneru Lakshmaiah Education Foundation, Guntur, (Andhra Pradesh), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 697-704 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2767037619/19©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: Disease Identification and management is a challenging task. Diseases of plants are commonly seen on the leaves of the plant. Precise Identification of the disease by visually observing them is difficult because of the complexity of the patterns on the leaf. So the demand for identifying the diseases using computers has raised more in recent years. This work employs a machine learning technique to identify the diseases of a tomato plant and suggest appropriate control measures to handle the disease. The system is designed using python software programmed into raspberry pi modules. After the image is uploaded for the disease identification, images are pre-processed using histogram equalization, filtering, color transformation and segmentation then the images are taken to the classification using SVM classifier and the appropriate disease identified is displayed on the screen along with the corresponding control measures to be taken
Keywords: Precision Farming, Machine Learning, Plant diseases, Database, Feature Extraction, Support Vector Machine.
Scope of the Article: Classification