38. Adaptive Neuro-Fuzzy Inference System-Based Yield Forecast Using Climatic Variables

Crop yield is affected by climate, prevailing during crop season and inputs applied. As such modeling, the cause and effect relationship between yield and these factors could provide an approach for pre-harvest yield forecast. Prediction of impacts of Climate Change (CC) on crop yields requires a model and its parameters, how crops respond to climate. Predictions from various models often disagree with the climatic variables and its impact. A common method is used to quantify impacts of CC is statistical models trained on historical yields and some simplified measurements of weather parameters, such as growing season average temperature and precipitation. CC is a really big apprehension to the entire world. Its direct impact on crop growth and yield is very important to understand. In the present study, the Fuzzy logic crop yield model was developed by considering different climatic variables. Temperature, rainfall, evaporation, humidity parameters were considered for the crop yield model. The model was developed by considering the 15-year crop yield data and the same period for the climatic variables. The triangular membership function is being adopted in the fuzzy model. In this study, a fuzzy rule-based system (FRBS) using the Takagi Sugeno-Kang approach has been used for developing the crop yield model. Model is validated by the coefficient of correlation and found that there is more than 0.9 coefficient of correlation between observed and evaluated yield.