A parameter based ANFIS model for crop yield prediction

Crop yield prediction is important as it can support decision makers in agriculture sector. It also assists in identifying the relevance of attributes which significantly affect the crop yield. Wheat is one of the widely grown crops around the world. Its accurate prediction can solve various problems related to wheat farming. This work analyses how yield of a particular crop is determined by few attributes. In this paper, the models of Fuzzy logic (FL), Adaptive Neuro Fuzzy Inference System (ANFIS) and Multiple Linear Regression (MLR) are used for predicting the yield of wheat by considering biomass, extractable soil water (esw), Radiation and rain as input parameters. The outcome of the prediction models will assist agriculture agencies in providing farmers with valuable information as to which factors contribute to high wheat yield. We compare all these models based on RMSE values. Results show that the ANFIS model performs better than MLR and FL models with a lower RMSE value.