MACHINE LEARNING APPROACH FOR KHARIF RICE YIELD PREDICTION INTEGRATING MULTI-TEMPORAL VEGETATION INDICES AND WEATHER AND NON-WEATHER VARIABLES
Keywords: Artificial Neural Network, Random Forest, NDVI, Kharif Rice, Yield Prediction, Purulia, Bankura
Abstract. The development of kharif rice yield prediction models was attempted through Machine Learning approaches such as Artificial Neural Network and Random Forest for the 42 blocks covering 13,141 sq km upland rainfed area of Purulia and Bankura district, West Bengal. Models were developed integrating monthly NDVI with weather and non-weather variables at block-level for the period 2006 to 2015. The model correlation obtained was 0.702 with MSE 0.01. Though the weather variables vs NDVI models are quite satisfactory, NDVI vs kharif rice yield models however, show relatively less correlation, about 0.6 revealing the requirement of varied additional farmer-controlled inputs. Development of NDVI vs crop yield models for different crop growth stages or fortnightly over a larger data set with selective adding of weather and non-weather variables to NDVI would be the most appropriate.