Vegetable price prediction using data mining classification technique

• Published in: 2012 International Conference on Pattern Recognition, Informatics and Medical Engineering pp. 99 - 102
• Main Authors: Nasira, G. M., Hemageetha, N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2012
• Subjects: Agriculture; Artificial neural networks; Back-propagation (BP); Biological neural networks; Data mining; Data models; Neural Networks; Neurons; Predictive models; Vegetable Price
• ISBN: 1467310379; 9781467310376
• DOI: 10.1109/ICPRIME.2012.6208294

Each and every sector in this digital world is undergoing a dramatic change due to the influence of IT field. The agricultural sector needs more support for its development in developing countries like India. Price prediction helps the farmers and also Government to make effective decision. Based on the complexity of vegetable price prediction, making use of the characteristics of neural networks such as self-adapt, self-study and high fault tolerance, to build up the model of Back-propagation neural network to predict vegetable price. A prediction model was set up by applying the neural network. Taking tomato as an example, the parameters of the model are analyzed through experiment. At the end of the result of Back-propagation neural network shows absolute error percentage of monthly and weekly vegetable price prediction and analyze the accuracy percentage of the price prediction.