TITLE: Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning 

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.
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.
Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning
http://arxiv.org/abs/2306.11946v1