Abstract
Accurate prediction of crop yield is crucial for optimizing agricultural practices and ensuring food security. This project presents a novel approach, titled "Soil Factors-Driven Crop Yield Prediction with Optimized GELM (Gaussian Extreme Learning Machine)", aimed at improving crop yield prediction by leveraging soil factors. In this study, we propose an optimized version of the Gaussian Extreme Learning Machine (GELM) algorithm to effectively model the complex relationship between soil factors and crop yield.
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