원문정보
초록
영어
In Agriculture pre harvest glut, post harvest loss and intermediary involvement cause producer to get the lowest price in the entire marketing process. Though the authorities provide long time monthly price series for various geographically spread regions of the country in websites, the lack of knowledge to process online market data does not allow farmers to take a dynamic decision for the sale of their produce. In this model, the equipped marketing information is analyzed for integrating domestic markets and manifesting price transmission from markets to farm gate (prices received by farmers) leading to sustainable profit. Market wise support price for crops is extracted from website tables using XML SAX(Simple API for XML) Expression and interested crop price associated with its season and variety are clustered using SONN (Self organized neural network). Then support vector regression (SVR) based price prediction for long and midterm movement helps farmers in the decision making of agricultural marketing. As all the processing is done at the back end it is easy for the farmers to interact with the system and reach for more accurate prediction. The results assure of more precision than other traditional methods such as regression techniques and moving averages
목차
1. Introduction
2. Architecture
2.1. Phase-I
2.2. Phase II
2.3. Phase III
2.4. Phase IV
3. Functional Model
3.1. Empirical Data
3.2. Extraction of Dynamic Marketing Information from Web Pages using DOM
3.3. SONN Clustering for Commodity Categorization
3.4. Support Vector Regression based Crop Price Prediction
4. Experimental Results
4.1. Performance Measurement
5. Conclusion
6. Future Enhancements
Acknowledgements
References