Comparative Study of Soft Computing Methodologies for Energy Input–Output Analysis to Predict Potato Production

Abstract

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to predict potato production in Iran. Data related to potato yield from 2010 to 2011 was collected from 50 potato producers in Hamedan, Iran. The resulting ANFIS network has an input layer with eight neurons and an output layer with a single neuron (potato yield). The energy inputs were manual labor, diesel, chemical fertilizers, and manure from farm animals, chemicals, machinery, water, and seed. The most significant and influential inputs were selected from the eight initial inputs and the ANFIS network was used to choose the parameters that have the most influence on potato yield. A new ANFIS model was created after the three most influential parameters were selected. The new ANFIS model was then utilized to estimate yield using the three energy inputs. Next, the ANFIS model results were compared with the results from the support vector regression (SVR) technique. The end results revealed that ANFIS provided more accurate predictions and had the capacity to generalize. The Pearson correlation coefficient (r) for ANFIS potato yield prediction was 0.9999 in the training and testing phases, while the SVR model had a correlation coefficient of 0.8484 in training and 0.9984 in testing.