Multi- Model Ensemble with Deep Neural Network Based Crop Yield Prediction

Abstract

Reliable prediction of crop yield is important in the creation of successful regional and global agricultural and food policies. When production has been predicted, farm inputs like fertilizers will vary according to plant and soil requirements. Different techniques have been proposed for accurate prediction of crop yield. Deep Neural Network (DNN) was introduced to understand the environmental factor i.e., weather data for accurate yield prediction. In order to enhance the accuracy of yield prediction, Multi-Model Ensemble with DNN (MME-DNN) is introduced where climate, weather and soil data are considered for yield prediction. A statistical model is used to find the variation of climate, weather and soil parameters from year-to-year. These variations are used for climate, weather and soil predictions and these predictions play an important role in yield prediction. The predicted climate, weather and soil parameters are given as input to DNN for yield prediction. The yield prediction accuracy is improved by considering various environmental data.