Plant Disease Identification using C-Means and SVM Classifier
K Rajasekhar1, V. Harinath2, S.Nandha kishor3
1K Rajasekhar , PG Scholar, B.Tech (ECE), Kuppam engineering college.
2Mr.V. Harinath , M.Tech, Associate Professor, Kuppam engineering college.
3Dr.S.Nandha kishor , M.Tech , Ph.D , Associate professor, Kuppam engineering college.

Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 2334-2337 | Volume-8 Issue-5, January 2020. | Retrieval Number: E5809018520/2020©BEIESP | DOI: 10.35940/ijrte.E5809.018520

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Agriculture is the backbone of India. In India 58% of people india are depends on agriculture. Vegetable are the most popular common crops in India. Many diseases are affected during the growing. To detect the vegetables plant leaf disease is more important because of fewer propensities. The vegetation production gets affected if correct care isn’t taken. Image process is one in all upbringing technology that helps to resolve such problems with varied algorithms and techniques. Most of the diseases of vegetable plants detected at primary stages as they have an effect on leaves 1st. detective work the diseases at the initial stage on leaves can sure as shooting avoid close loss. During this project, we tend to area unit characteristic the sickness victimization image segmentation and also the SVM rule. to spot the pathological half in leaf, image segmentation is employed. And for classification of correct sickness, Multi-class SVM rule is employed. In the last stage of the disease, detection is recommended by the User for treatment. Automatic disease detection has many benefits to monitor and control the large fields to detect the disease automatically. By using the pesticide minimize the economic loss and identify the disease. This project is implemented by using Digital image processing and it can recognize the problems in crops from images, based on colors and shape to detect the disease automatically. We can rectify the problem fast and accurate manner. The image processing (Digital) technique is used to magnify the image. Here in this project, we are introducing the IoT based smart farming using the Raspberry PI and sensors with the image process. Here we will capture the images of tomato leaves with cam which is connected with raspberry pi that captured images will be sent to email id and that images are done using image processing in MAT lab software.
Keywords: Image Processing, Python Language, Plant Disease Identification.
Scope of the Article: Natural Language Processing and Machine Translation.