Cultivators are always curious about the factors affecting yield in plant production. Determining these factors can provide information about the yield in the future. The 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 artificial neural networks.
In this study, we used an artificial neural network method to model the production of cotton. From a comprehensive datum collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was 559.19 kg da-1. There are four factors that are selected as pivotal inputs into this model. As a result, the ultimate ANN model is able to forshow cotton production, which is built on elements such as 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.
This preprint is available for download as a PDF.