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Related Experiment Videos

Design and evaluation of bayesian optimized hybrid deep learning model for forecasting crop yields using climate

Nadia Mushtaq1, Atef F Hashem2, Mahnoor Irfan3

  • 1Department of Statistics, Forman Christian College (A Chartered University), Lahore, Pakistan.

Scientific Reports
|June 29, 2026
PubMed
Summary

Related Concept Videos

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

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A new Bayesian optimized hybrid deep learning model accurately predicts wheat and rice yields using climate data. This AI-driven approach enhances agricultural planning for climate uncertainty.

Area of Science:

  • Agricultural Science
  • Climate Science
  • Artificial Intelligence

Background:

  • Accurate crop prediction is crucial due to climate change impacts on agriculture.
  • Existing models struggle to capture complex climate influences on crop yields.
  • Long-term climate data (1961-2021) on temperature, CO2, and precipitation in Pakistan is available.

Purpose of the Study:

  • To develop an advanced AI model for precise crop yield prediction.
  • To improve upon existing models in understanding intricate climate-agriculture interactions.
  • To forecast future agricultural production for wheat and rice.

Main Methods:

  • A hybrid deep learning model incorporating Bayesian Optimization (BO) was developed.
  • The model integrates temporal and climatic patterns influencing crop production.
Keywords:
Bayesian optimizationClimate dynamicsCrop productionHybrid deep learning model

Related Experiment Videos

  • Performance was evaluated against Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and Vector Autoregression (VAR) models.

    • Main Results:

    • The proposed Bayesian Optimized VAR-BiLSTM hybrid model demonstrated superior accuracy and stability.
    • Achieved a coefficient of determination (R²) of 0.9611 and a Mean Absolute Percentage Error (MAPE) of 8.01%.
    • The model exhibits significant forecasting power for climate-driven crop production.

    Conclusions:

    • The developed AI model offers a scalable solution for resilient agriculture amidst climate uncertainty.
    • Provides data-driven insights for policymakers and agricultural planners to adapt strategies.
    • Enhances AI applications in agriculture for improved food security and management.