Spark Architecture and Ensemble‐Based Feature Selection With Hybrid Optimisation Enabled Deep Long Short‐Term Memory for Crop Yield Prediction

Spark Architecture and Ensemble‐Based Feature Selection With Hybrid Optimisation Enabled Deep Long Short‐Term Memory for Crop Yield Prediction Anitha Rajathi Surendran Department of CSE Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India https://orcid.org/0000-0002-6255-3503 Arun Sahayadhas Department of CSE Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India ABSTRACT

Precise prediction of crop yield is crucial for addressing the economic resilience and food security of agricultural countries. Current models for crop yield prediction struggle to fully understand the long‐term trends and seasonal variations. Here, the Fractional Rider‐Based Water Cycle Algorithm‐Based Deep Long Short‐Term Memory (FRWCA‐DLSTM) is devised for crop production forecasting and addresses these issues. Primarily, the simulation of the IoT is performed. Then, the selection of Cluster Head (CH) and routing are done with the Rider‐Based Water Cycle