Crop Prediction Based on Influencing Parameters for Different States in India- The Data Mining Approach

Agriculture plays a crucial role for the production of food in Indian regions. Indian regions mainly produces crops like rice, wheat, maize and many other types of crop. It is generally known that, the soil, climate, pesticides, Fertilizers and ground water is influencing the essential factor for enhancing the productivity of any crop. Let us consider soil, which is the key element involved in providing nutrients for proper development and growth of crops. Secondly, climate is also having major role in agriculture as crop growth depends on rainfall, humidity, temperature etc. Thirdly, Pesticides is widely used to control pest and prevents the damage of crops. Fourthly, Fertilizers can improve the quality of crops. Finally, ground water enriches the nutrients in soil. The main goal of this research work is to know about the ways to improve the crop prediction by using data mining techniques, which in turn help the farmers to take better decision. The current study centres around different information mining procedures utilized in various conditions of India and anticipate future harvest along-side reasonable information mining calculation saw during the period(1920-2019). The parameters considered for the examination were soil, atmosphere, water thickness, pesticides and composts and Crop informational collection. The Classification calculations utilized in study were Adaptive boosting classification, Excess tree classification, neural based classification, Multiple Process classification, Decision making classification, K-closest neighbours, Bayesian theory classification, decision Forest classification, support group machine, and Randomized Gradient Classification. The Experimental results show predicted crop, suitable algorithm and algorithm accuracy in that particular state of India respectively.