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Sidnal, Nandini
- Prediction of Future Market Price for Agricultural Commodities
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Authors
Affiliations
1 Department of Computer Science and Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, IN
2 Department of Computer Science Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, IN
3 Department of Computer Science Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, IN
1 Department of Computer Science and Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, IN
2 Department of Computer Science Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, IN
3 Department of Computer Science Engineering, KLE Dr. M S Sheshgiri College of Engineering and Technology, Belgaum, Karnataka, IN
Source
International Journal of System & Software Engineering, Vol 3, No 1 (2015), Pagination: 10-17Abstract
The agricultural commodity prices have a volatile nature which may increase or decrease inconsistently causing an adverse effect on the economy. The work carried out here for predicting prices of agricultural commodities is useful for the farmers because of which they can sow appropriate crop depending on its future price. Agriculture products have seasonal rates; these rates are spread over the entire year. If these rates are known/alerted to the farmers in advance, then it will be promising on ROI (Return on Investments). It requires that the rates of the agricultural products updated into the data set of each state and each crop; in this application five crops are considered. The predictions are done based on neural networks Neuroph framework in java platform and also the previous year's data. The results are produced on mobile application using android. Web based interface is also provided for displaying processed commodity rates in graphical interface. Agricultural experts can follow these graphs and predict market rates which can be informed to the farmers. The results will be provided based on the location of the users of this application.Keywords
Commodity Price, Neural Network, Back Propagation, Prediction, Agriculture, Dataset.
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