Home > Engineering > Electronics & Communication Engineering > Volume-1 > Issue-5 > An Adaptive Model to Classify Plant Diseases Detection using KNN

An Adaptive Model to Classify Plant Diseases Detection using KNN

Call for Papers

Volume-3 | Issue-6

Last date : 27-Oct-2019

Best International Journal
Open Access | Peer Reviewed | Best International Journal | Indexing & IF | 24*7 Support | Dedicated Qualified Team | Rapid Publication Process | International Editor, Reviewer Board | Attractive User Interface with Easy Navigation

Journal Type : Open Access

Processing Charges : 700/- INR Only OR 25 USD (for foreign users)

Paper Publish : Within 2-4 Days after submitting

Submit Paper Online

For Author

IJTSRD Publication

Research Area

An Adaptive Model to Classify Plant Diseases Detection using KNN

Rajneet Kaur | Ms. Manjeet Kaur


Rajneet Kaur | Ms. Manjeet Kaur "An Adaptive Model to Classify Plant Diseases Detection using KNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5, August 2017, pp.1233-1239, URL: https://www.ijtsrd.com/papers/ijtsrd2424.pdf

Fungi and bacteria can interact synergistically to stimulate plant growth through a range of mechanisms that include improved nutrient acquisition and inhibition of fungal plant pathogens. These interactions may be of crucial importance within sustainable, low-input agricultural cropping systems that rely on biological processes rather than agrochemicals to maintain soil fertility and plant health. Although there are many studies concerning interactions between fungi and bacteria, the underlying mechanisms behind these associations are in general not very well understood, and their functional properties still require further experimental confirmation. This proposal is about automatic detection of Fungi diseases and diseased part present in the leaf images of plants and even in the agriculture Crop production. It is done with advancement of computer technology which helps in farming to increase the production. Mainly there is problem of detection accuracy and in neural network approach support vector machine (SVM) is already exist. In this research proposal, we have discussed the various advantages and disadvantage of the plant Fungi diseases prediction techniques and proposed a novel approach (KNN) for the detection algorithm, a framework of our proposed work is given in this proposal and methodology is included.

SVM, Enhanced SVM, PCA, KNN Approach, Training Dataset, Train Dataset.

Volume-1 | Issue-5, August 2017
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

Thomson Reuters
Google Scholer