Barley Yield Forecasting Based on Remote Sensing Data and Xgboost and SVM Machine Learning Algorithms

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agricultural Technology, College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.

2 Department of Electrical and Computer Engineering, University of Denver, Denver, Colorado, US.

10.22059/jwim.2023.360327.1083

Abstract

In recent years, the use of machine learning algorithms has been a promising way to improve crop yield predictions, especially when using non-linear relationships. This research was conducted with the aim of evaluating the yield estimation of irrigated and rainfed barley as well as the total yield of barley produced in the provincial centers of Iran using remote sensing data and machine learning methods including XGBoost and SVM. The results showed that by using climatic data, drought indices and plant indices of remote sensing and also XGBoost and SVM algorithms, it is possible to reliably estimate barley yield in different regions of the country with different climates. In general, the RMSE error obtained for both models was acceptable (0.41 and 0.77 t/ha). The R2 determination coefficient values for XGBoost and SVR algorithms in modeling rainfed barley cultivation performance were equal to 0.2 and 0.22 respectively, in irrigated barley performance were equal to 0.52 and 0.55 and for total barley were equal to 0.66 and 0.65, indicating that the rainfed yield modeling were not as suitable as irrigated and total barely yield modeling. The RBF kernel was chosen as the best kernel to use for the SVM algorithm. Also, in this research, while examining the effects of change in train and test data dividing, the parameters of precipitation, temperature, and evapotranspiration were determined as the most important parameters affecting the performance of the barley yield for both algorithms in different evaluated conditions.

Keywords

Main Subjects

References

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Water and Irrigation Management
Volume 13, Issue 4
January 2024
Pages 1119-1137

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