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Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

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Statistical and machine learning models for location-specific crop yield prediction using weather indices.

Ajith S¹, Manoj Kanti Debnath², Karthik R³

¹Department of Agricultural Statistics, Uttar Banga Krishi Viswavidyalaya, Cooch Behar, India. ajithagristat@gmail.com.

International Journal of Biometeorology

|August 31, 2024

Summary

Accurate crop yield prediction requires integrating weather data. Nonlinear machine learning models like Support Vector Regression (SVR) and Artificial Neural Networks (ANN) show superior performance for location-specific forecasting.

Keywords:

Artificial Neural Network Hyperparameter Optimization Partial Least Square Regression Penalized regression models Support Vector Regression Yield prediction

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Area of Science:

Agricultural Science
Environmental Science
Data Science

Background:

Crop yield prediction is crucial for agricultural stakeholders.
Crop development is intrinsically linked to weather variables, necessitating meteorological data integration.
Climate-yield relationships exhibit significant local variations, favoring district-level modeling.

Purpose of the Study:

To systematically review statistical and machine learning models for crop yield prediction using weather factors.
To identify commonly applied and high-performing models for location- and crop-specific yield forecasting.
To explore the suitability of different modeling approaches for capturing complex climate-yield interactions.

Main Methods:

Systematic literature review of statistical and machine learning models.
Analysis of models commonly used for crop yield prediction with meteorological data.
Evaluation of model performance based on reported success ratios and applicability.

Main Results:

Artificial Neural Network (ANN) and Multiple Linear Regression were the most frequently applied models.
Support Vector Regression (SVR) demonstrated a high success ratio, performing well across various applications.
Nonlinear models, specifically SVR and ANN, outperformed others, indicating complex nonlinear relationships between weather and crop yield.

Conclusions:

Nonlinear machine learning models like SVR and ANN are highly effective for crop yield prediction.
Model optimization, including hyperparameter tuning in SVR and activation functions/neurons in ANN, enhances predictive accuracy.
District-level modeling incorporating optimized statistical and machine learning techniques is essential for precise, location-specific crop yield forecasting.