Wheat yield prediction: Artificial neural network based approach
Muhd Khairulzaman Abdul Kadir, Mohd Zaki Ayob, Nadaraj Miniappan · 2014
Wheat yield prediction modeling is an important area of study because of its potential contribution to food security since it may be perceived to be a good indicator for global food availability. Many studies have been conducted in order to determine the best models for wheat yield prediction using various types of data which are available; these models include CERES-Wheat model, SIRIUS model and AFRCWHEAT2 model. In this study, our wheat yield prediction model is designed using a Multi-Layer Perceptron (MLP) backpropagation-based- feed forward artificial neural network (ANN). The data used was weather data including: sun, frost, rain and temperature as the input parameters from year 1997–2007. The output parameter of the model is using the wheat yield data for the years 1997–2007. The data is divided into three separate sets; — for training, validation and testing. Our MLP was able to predict, wheat yield with an accuracy of 98 %. Hence our MLP based wheat yield prediction model shows great promise as a tool which will be able to provide relatively accurate wheat yield prediction and may be applied to other crops.