Crop Production Analysis: ANFIS vs. Traditional Methods in Predictive Modelling

Nowadays, crop production is urgently needed as the population is rapidly increasing. As time goes by, there are more and more people to feed, creating an ever-increasing demand for limited resources. The reality is that current rates of agricultural production do not meet our needs. With population growth, food security has become an alarming problem, especially in poor areas. Without adequate crop production, millions of people would starve. In this research, an analysis has been done to find out which model yields less error while predicting crop yield. Linear regression, LASSO, decision tree, random forest and ANFIS (Adaptive Neuro Fuzzy Inference System) were applied on a standard dataset. Generated RMSE (Root Mean Square Error) values were compared. Fuzzy logic provides tolerance for inaccuracies, while neural networks provide the ability to find insights in data, making ANFIS more effective than other models in the category.