Use of Data Mining Technique for Prediction of Crop Disease & Yield in The Face of Climate Change and Market Analysis Strategy

Abstract

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 Neighbour 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 Neighbour 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.