Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning
This work addresses crop yield prediction for food security in the UK, but it is incremental as it applies existing methods to new data combinations without introducing novel techniques.
The study tackled winter wheat crop yield prediction in the UK by applying machine learning models to multiple heterogeneous datasets, such as soil and weather data at a zone-based level, and found that combining these datasets improved prediction results, though no specific numbers were provided.
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.