Sugarcane Yield Forecasting Using Artificial Neural Network Models

Neural networks have gained a great deal of importance in the area of soft computing and are widely used in making predictions. The work presented in this paper is about the development of Artificial Neural Network (ANN) based models for the prediction of sugarcane yield in India. The ANN models have been experimented using different partitions of training patterns and different combinations of ANN parameters. Experiments have also been conducted for different number of neurons in hidden layer and the algorithms for ANN training. For this work, data has been obtained from the website of Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. In this work, the experiments have been conducted for 2160 different ANN models. The least Root Mean Square Error (RMSE) value that could be achieved on test data was 4.03%. This has been achieved when the data was partitioned in such a way that there were 10% records in the test data, 10 neurons in hidden layer, learning rate was 0.001, the error goal was set to 0.01 and traincgb algorithm in MATLAB was used for ANN training.