Comparison of Machine Learning Methods for Predicting Winter Wheat Yield in Germany

05/04/2021
by   Amit Kumar Srivastava, et al.
7

This study analyzed the performance of different machine learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. To address the seasonality, weekly features were used that explicitly take soil moisture conditions and meteorological events into account. Our results indicated that nonlinear models such as deep neural networks (DNN) and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models. The results also revealed that the deep neural networks often had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. As a result, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). The feature selection method estimated the individual effect of weather components, soil conditions, and phenology variables as well as the time that these variables become important. As such, our study indicates which variables have the most significant effect on winter wheat yield.

READ FULL TEXT

page 5

page 6

page 13

page 14

page 15

page 16

page 18

page 19

research
02/07/2019

Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such...
research
08/17/2023

Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level

We introduce a simple yet effective early fusion method for crop yield p...
research
11/17/2021

A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction

Climate change is posing new challenges to crop-related concerns includi...
research
04/14/2023

Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction

Explainability in yield prediction helps us fully explore the potential ...
research
07/11/2023

Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks

Providing a model that achieves a strong predictive performance and at t...
research
08/28/2023

Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios

This study extensively compares conventional machine learning methods an...
research
07/01/2021

A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data

The presence of snow and ice on runway surfaces reduces the available ti...

Please sign up or login with your details

Forgot password? Click here to reset