Maize yield and nitrate loss prediction with machine learning algorithms

@article{Shahhosseini2019MaizeYA,
  title={Maize yield and nitrate loss prediction with machine learning algorithms},
  author={Mohsen Shahhosseini and Rafael A. Martinez-Feria and Guiping Hu and Sotirios V. Archontoulis},
  journal={Environmental Research Letters},
  year={2019},
  volume={14}
}
Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression… 

Figures and Tables from this paper

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt
TLDR
It has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML, indicating that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia
Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on
Forecasting Corn Yield With Machine Learning Ensembles
TLDR
A machine leaning based framework to forecast corn yields in three US Corn Belt states considering complete and partial in-season weather knowledge is provided and it is suggested that weather features corresponding to weather in weeks 18–24 (May 1st to June 1st) are the most important input features.
Prediction of Oil Palm Yield Using Machine Learning in the Perspective of Fluctuating Weather and Soil Moisture Conditions: Evaluation of a Generic Workflow
TLDR
The means of machine learning have great potential for the application to predict oil palm yield using weather and soil moisture data and most influential features that contributed to the prediction process are rainfall, cloud amount, number of rain days, wind speed and root zone soil wetness.
A Geographically Weighted Random Forest Approach to Predict Corn Yield in the US Corn Belt
TLDR
The proposed Geographically Weighted Random Forest Regression approach to improve crop yield prediction at the county level in the US Corn Belt outperforms other machine learning algorithms and can be potentially used to improve yield prediction for other types of crops in other regions.
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
TLDR
This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019 to suggest nonlinear models were more effective in understanding the relationship between the crop yield and input data.
Comparison of Machine Learning Methods for Predicting Winter Wheat Yield in Germany
TLDR
This study analyzed the performance of different machine learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology, and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time).
Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
Proximal sensing techniques can potentially survey soil and crop variables responsible for variations in crop yield. The full potential of these precision agriculture technologies may be exploited in
...
...

References

SHOWING 1-10 OF 121 REFERENCES
Predictive ability of machine learning methods for massive crop yield prediction.
TLDR
Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.
Predictive ability of machine learning methods for massive crop yield prediction
TLDR
Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.
Machine learning methods for crop yield prediction and climate change impact assessment in agriculture
Crop yields are critically dependent on weather. A growing empirical literature models this relationship in order to project climate change impacts on the sector. We describe an approach to yield
Random Forests for Global and Regional Crop Yield Predictions
TLDR
Random Forests was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared, and may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.
Crop Yield Prediction Using Deep Neural Networks
TLDR
A deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques was designed and significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT).
Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation
TLDR
The APSIM model can be used as a tool to assist N management guidelines in the US Midwest and five avenues on how the model can add value toward agronomic, economic, and environmental sustainability are identified.
Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture
TLDR
Results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field.
Multimodel ensembles improve predictions of crop–environment–management interactions
TLDR
The empirical results are based on five MME studies applied to wheat and show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables.
Seasonal crop yield forecast: Methods, applications, and accuracies
...
...