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Found 6 papers, 1 papers with code
Corn Yield Prediction with Ensemble CNN-DNN
Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles.
Improved Weighted Random Forest for Classification Problems
Several studies have shown that combining machine learning models in an appropriate way will introduce improvements in the individual predictions made by the base models.
Coupling Machine Learning and Crop Modeling Improves Crop Yield Prediction in the US Corn Belt
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt.
Forecasting Corn Yield with Machine Learning Ensembles
The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields.
Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
To this end, an optimization-based nested algorithm that considers tuning hyperparameters as well as finding the optimal weights to combine ensembles (Generalized Weighted Ensemble with Internally Tuned Hyperparameters (GEM-ITH)) is designed.
Maize Yield and Nitrate Loss Prediction with Machine Learning Algorithms
We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information?
