“CROP DISEASE PREDICTION”
Keywords:
Data mining, Machine Learning, Classification, ClusteringAbstract
Data mining and machine gaining knowledge of is an emerging field of research in facts era in addition to in
agriculture. Agrarian sector is facing rigorous trouble to maximize the crop productiveness. The present have a look at
makes a specialty of the packages of data mining strategies in crop sickness prediction in the face of climatic trade to
assist the farmer in taking choice for farming and accomplishing the predicted monetary go back. The Crop disease
prediction is a prime hassle that may be solved based totally on available data. Data mining strategies are the better
selections for this purpose. Exclusive data mining techniques are used and evaluated in agriculture for estimating the
future year’s crop production. The main cause of the gadget is for social use. Farmer has to face many troubles like lack
of know-how, Manures, fertilizers and Agriculture marketing etc. gift method SAR Tomography takes the photographs
and gives the exceptional development stages of crop. This system not gives the fertilizers and precautions to the farmer.
This paper gives quick analysis of crop disease prediction the usage of k Nearest Neighbor class approach and Density
based clustering approach for the chosen place. The styles of crop production in response to the climatic (rainfall,
temperature, relative humidity and sunshine) impact across the selected regions are being evolved using ok Nearest
Neighbor technique. For that reason it is going to be useful if farmers should use the technique to are expecting the
future crop productivity and therefore adopt opportunity adaptive measures to maximize yield if the predictions fall
below expectations and business viability.