Application of Artificial Intelligence Methods to Predict Cotton Production in Turkey

Abstract

Farmers are always curious about the factors affecting yield in plant production. Determining these factors can give information about the yield in the future. Reliability of information is dependent on a good prediction model. According to the operating process artificial neural networks imitate the neural network in humans. The ability to make predictions for the current situation by combining the information people have gained from different experiences is designed in artificial neural networks. Therefore, in complex problems, it gives better results than conventional statistical methods.
 In this study, artificial neural networks and support vector machines methods of artificial intelligence were used in order to predict the production of cotton. From a comprehensive data collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was prevised at 559.19 kg da-1. There is four factors that picked as pivotal input into this model. As a result, the ultimate artificial neural network model is able to foreshow cotton production, which is built on elements like: farm states (cotton area and irrigation periodicity), machinery usage and fertilizer consumption. At the end of the study, cotton yield was estimated with %84 accuracy.