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Fertilizer value updation has a positive practical significance for guiding agricultural production and for notifying the change in market rate of fertilizer to the farmer. The concept of this paper is to implement the crop selection method so that this method helps in solving many agriculture and farmers problems. This improves our Indian economy by maximizing the yield rate of crop production. Different types of land condition. So the quality of the fertilizers are identified using ranking process. By this process the rate of the low quality and high quality fertilizer is also notified. The usage of ensemble of classifiers paves a path way to make a better decision on predictions due to the usage of multiple classifiers. Further, a ranking process is applied for decision making in order to select the classifiers results. This system is used to predict the crop for further.

In the world of developing technologies, the success of sharing information will help the agriculturists in realizing and developing their potential. The information sharing is that the valuable and timely information is being shared between agriculturists, either formally or informally. The willingness of information sharing refers to the open attitude among agriculturists. This open attitude determines the degree and scope of information sharing. Using web-technologies like html and css we build the web application, We create dataset by gathering data from multiple resources and place them in place which is used to predict the price of the fertilizers and reults are subjected to non-linear test later priorities are set and rankings are given to the list of fertilizers. Place information in our application and share that information to agriculturists whose data is collected and stored in the mysql server. we software to
automatically send the updated information to the agriculturists in the form of text that agriculturists no need to go to near by towns and cities to know the updated information. We will be machine learning algorithms to predict the price of the fertilizers for the next two months. For prediction purpose we will be using machine learning algorithms to predict the crop for the further usage of the agriculturists. Further, a ranking process is applied for decision making in order to select the classifiers results.
>Data set collection from various sources.

> Data parsing and cleansing technique is applied to make the raw data into processing data.
> The data collected is subject to machine learning system along with run time analysis makes an efficient fertilizer value updation system.
> Usage of Ensemble of classifiers makes the model more robust and efficient.
> Ranking technique used in the project helps us to make efficient decisions.
> Creating a web application for user registrations and collection of data

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