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A Hybrid Approach to Tea Crop Yield Prediction Using Simulation Models and Machine Learning.

Dania Batool¹, Muhammad Shahbaz¹, Hafiz Shahzad Asif²

¹Department of Computer Engineering, University of Engineering and Technology, Lahore 58590, Pakistan.

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|July 27, 2022

Summary

Machine learning algorithms outperform crop simulation models for predicting tea yield, offering a more efficient approach. This study compared the FAO AquaCrop model with machine learning techniques for accurate tea yield forecasting.

Keywords:

AquaCrop crop simulation models crop yield prediction machine learning tea yield

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Area of Science:

Agricultural Science
Crop Modeling
Machine Learning Applications

Background:

Tea (Camellia sinensis L.) is a globally significant beverage crop, necessitating accurate yield prediction for import/export management.
Existing tea yield prediction methods lack comparative analysis between crop simulation models and machine learning techniques.
There is a need to evaluate different data types for calibrating simulation models and training predictive algorithms.

Purpose of the Study:

To conduct a comparative analysis of tea yield prediction methods.
To evaluate the performance of the Food and Agriculture Organization (FAO) AquaCrop simulation model against machine learning techniques.
To assess the impact of different data types on prediction accuracy.

Main Methods:

Calibration of the FAO AquaCrop simulation model using weather, soil, crop, and agro-management data (2016-2019).
Training and evaluation of ten regression algorithms using the same dataset.
Utilizing 10-fold cross-validation and train-test split for model performance assessment.

Main Results:

The FAO AquaCrop model calibration resulted in Mean Absolute Error (MAE) of 0.45 t/ha, Mean Squared Error (MSE) of 0.23 t/ha, and Root Mean Square Error (RMSE) of 0.48 t/ha.
Machine learning models achieved superior performance, with the best model yielding MAE of 0.093 t/ha, MSE of 0.015 t/ha, and RMSE of 0.120 t/ha (10-fold cross-validation).
XGBoost regressor with train-test split achieved MAE of 0.123 t/ha, MSE of 0.024 t/ha, and RMSE of 0.154 t/ha.

Conclusions:

Machine learning regression algorithms demonstrate superior performance in tea yield prediction compared to crop simulation models, especially with limited data.
The study highlights the potential of integrating crop simulation models and machine learning for enhanced tea yield prediction.
This research provides a valuable framework for improving tea yield forecasting by leveraging diverse data sources.