Automated Rice Crop Yield Prediction using Sine Cosine Algorithm with Weighted Regularized Extreme Learning Machine

Crop yield prediction is a not a simple task in precision agriculture. To be specific, paddy is one of the globally significant cereal crops and extremely important for decision-making and crop management. Although there exist more crop yield prediction methods, but lacks a better outcome in paddy yield prediction. Attaining optimal crop yield was always a difficult task for the farmer because of changeable ecological conditions. The main factor for uncertainty of crop yield are changing nature of weather, land types, and availability of resources. Hence, the scientists around the world making continuous efforts to find methods, which can precisely and efficiently predict the crop, yield in much advance such that the agronomists can make best course of action to encounter the future challenges. Rice yield prediction includes estimation of the amount of rice that would be produced in a specific area, generally a region or a farm, related to several factors that could affect the crop yield. Therefore, this article introduces an Automated Rice Crop Yield Prediction using Sine Cosine Algorithm with Weighted Regularized Extreme Learning Machine (SCA-WRELM). The proposed SCA-WRELM technique aims to forecast the rice crop productivity effectually. To do this, the SCA-WRELM technique performs min-max data normalization process to scale the data into uniform format. For yield prediction, the SCA-WRELM technique uses WRELM model to forecast the rice productivity. Finally, the SCA is exploited for the optimal parameter tuning of the WRELM model and it results in improved predictive results. The simulation values of the SCA-WRELM approach is tested on rice yield dataset and the outcomes demonstrate the improved outcomes of the SCA-WRELM approach over other existing methods.