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Design and Analysis of an Effective Channel Distribution Approach for Agricultural Commodities using MongoDB
Objectives The farmers are getting far less cost for their farming products since they didn’t know where to offer for the best cost. This issue could be settled by this paper. Methods/Statistical Analysis: We worked with Historical Data of various agricultural commodities production, demands and pricing at different locations using MongoDB, a NoSQL database tool. A Module where the Buyers of Agricultural Commodities would enter their need and the costs they offer, for commodities. Demand qualities and Current Price datasets are created. The calculations for each of the Farmer Module and Buyer Module were composed and executed to satisfy the point of guaranteeing that the farmers can offer his wares at the ideal cost. Findings: Making a Module, where the Buyers of Agricultural Commodities would enter their need and the costs they will offer, store these points of interest and in light of these subtle elements, Demand qualities, and Current Price datasets are created. Farmer modules were created with the available agricultural commodities and their cost, a location of availability. The calculations for each of the Farmer Module and Buyer Module were composed and executed effectively. The calculations were tried for different situations and the normal results were figured it out. Examine the information and the farmer’s need (i.e) estimation called as horticulture yield investigation, and in this way discover the different valuable examples and making sense of the best methodologies at the agriculturists to cost and offer their yields in various locales at various times of the year. To satisfy the point of guaranteeing that the rancher can offer his wares at the most ideal cost. Applications/Improvements: This application could be moved to the cloud with MongoDB server and the farmers and purchasers will be given a separate login id. Total system may be available in web.
Agriculture Business, Agricultural Commodities, Big Data, NoSQL, MongoDB.
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