Estimation of optimal fertilizers for optimal crop yield by adaptive neuro fuzzy logic

To analyze the crop yield there is need to estimate the crop production. However, it is challenging task to control the crop production response because of different inputs. Fertilizer has a notable impact on crop yield. In order to analyze the fertilizers, it is suitable to establish a predictive approach to obtain optimal parameter for the best fertilizers. The main goal of the study was to establish a predictive approach by adaptive neuro fuzzy inference system (ANFIS) to determine the impact of temperature, humidity, moisture, soil type, crop type, nitrogen, potassium and phosphorous on the fertilizers prediction. There are five fertilizers which should be predicted by the ANFIS. The used fertilizers are: urea, DAP, 14-35-14, 28-28, 17-17-17, 20-20, 10-26-26. It was found that the “phosphorous” and “nitrogen” is the optimal combination of two parameters for the fertilizer prediction. The results could be useful for optimization of the crop yield response in order to reduce the cost of the process.