Wheat Yield Prediction Using Feedforward Neural Networks

Agriculture crop prediction is a challenging and interesting problem domain for the computer community. The computer machine helps to improve the productivity of the crop with such predication. This paper illustrates a research model which provides the predications of the wheat crop in the Rajasthan region of India. This paper uses feedforward neural network models to accomplish the task of predication for the wheat crop. These models are trained with three different feedforward neural network architectures namely multi-layer perception feedforward generalized regression and radial basis neural network. There is feature scaling method was used for normalization of large previous year's crop detail data which has been obtained by agriculture department of Rajasthan government. The simulation results indicate the better prediction with 97.75
^%
accuracy found by the feedforward neural network model trained with backpropagation learning rule in comparison to Generalized regression neural network and Radial basis neural network.