A Soft Computing Model to Predict the Rice Production in India
Surjeet Kumar1, Manas Kumar Sanyal2
1Priya Vaijayanthi R, Department of Computer Science and Engineering, GMR Institute of Technology, Rajam, (Andhra Pradesh), India.
2Raja Murugadoss J, Department of Civil Engineering, GMR Institute of Technology, Rajam, (Andhra Pradesh), India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 796-800 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8012088619/2019©BEIESP | DOI: 10.35940/ijeat.F8012.088619
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: India, which has the most rice tillage area in the world, is one of the massive cultivators of this white crop. Besides, rice is the main staple food of many Indians. The main purpose of this study is to develop a predictive model on Indian rice production. In this, we have used different types of soft computing models like Fuzzy Logic, Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and developed a hybrid model to get the optimum result. The vital aspect of this predictive model is the accuracy of the future data prediction on the basis of past time series data. The Prediction performance has been assessed by using error finding equations like Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Average Error.
Keywords: Fuzzy Logical Relationships, Statistical Equations, Artificial Neural Network (ANN), Genetic Algorithm (GA), and Error Calculations.