An Ensemble model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty


  • Dhanya K
  • B.Jayanthi,HOD


A global food crisis is starting to loom as the world's population rises, more people select diets high in meat and dairy products, and more acreage is dedicated to the development of biofuel crops. Issues with the global biophysical and socioeconomic systems both affect harvests. Using the best management practices, production gaps brought on by changes in crop performance can be filled. It is crucial to food security, especially during times of drought, flooding, or other natural disasters. To increase agricultural yield, the proper crop must be selected before being sown. The features from several datasets were found and the data was integrated to handle crop yield prediction. Proposed ensemble models for improving prediction rate: EABRDTR, EBCKNN, EABRRFR, and EGBRDTR. EGBRDTR exhibits strong prediction and R2 score >0.80 <0.95.