Yield prediction models can be divided between data-driven and process-based models
(crop growth models). The first category contains many different types of models with …

Abstract The European Union is highly dependent on soybean imports from overseas to
meet its protein demands. Individual Member States have been quick to declare self …

Feature selection wrapper methods are powerful mechanisms for reducing the complexity of
prediction models while preserving and even improving their precision. Meta-heuristic …

The prevention of soil salinization and managing agricultural irrigation depend greatly on
accurately estimating soil salinity. Although the long-standing laboratory method of …

Soybean genotypes have distinct physicochemical characteristics, mainly regarding the oil
and protein contents in the grains. The use of high-throughput phe-notyping technologies …

Improving the urban wind climate yields substantial advantages, encompassing enhanced
public health, increased pedestrian safety, improved building energy efficiency, and effective …

Dataset quality heavily impacts the predictive performance of data-driven modelling. This
issue can be exacerbated in the prediction of agricultural production due to the complex …

Accurately estimating large-area crop yields, especially for soybeans, is essential for
addressing global food security challenges. This study introduces a deep learning …

The accurate prediction of soybean yield is of great significance for agricultural production,
monitoring and early warning. Although previous studies have used machine learning …

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting
models. However, it is still challenging to identify the most critical features from a dataset …