David Lillis: Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets Using Machine Learning
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Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets Using Machine Learning

Yogesh Bansal, David Lillis and Tahar Kechadi

In 2022 International Conference on Computational Science and Computational Intelligence (CSCI'22), pages 206--212, Las Vegas, Nevada, USA, Dec. 2022. IEEE Computer Society.

Abstract

Winter wheat is one of the most important crops in the United Kingdom, and crop yield prediction is essential for the nation's food security. Several studies have employed machine learning (ML) techniques to predict crop yield on a county or farm-based level. The main objective of this study is to predict winter wheat crop yield using ML models on multiple heterogeneous datasets, i.e., soil and weather on a zone-based level. Experimental results demonstrated their impact when used alone and in combination. In addition, we employ numerous ML algorithms to emphasize the significance of data quality in any machine-learning strategy.