Abstract :
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.
By 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,
URL: http://www.ijtsrd.com/papers/ijtsrd2424.pdf
Paper URL: http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/2424/an-adaptive-model-to-classify-plant--diseases-detection-using-knn/rajneet-kaur
Keyword :
SVM, Enhanced SVM, PCA, KNN Approach, Training Dataset, Train Dataset.