Enhancing rice yield prediction: a deep fusion model integrating ResNet50-LSTM with multi source data
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a deep learning model (ResNet50-LSTM) to accurately forecast rice yields in Pakistan using satellite data and climate information. The hybrid model shows high accuracy, aiding global crop yield estimation and food security.
Area Of Science
- Agricultural Science
- Computer Science
- Environmental Science
Background
- Global food security relies heavily on rice production, a key crop in Pakistan facing climate change and pandemic impacts.
- Accurate rice yield prediction is crucial for economic stability and informed decision-making in Pakistan.
- Existing predictive models need enhancement to address the complexities of climate change and pandemics on crop yields.
Purpose Of The Study
- To develop and evaluate an innovative deep learning-based hybrid predictive model for forecasting rice yields in Pakistan.
- To utilize multi-modal data, including satellite imagery (EVI, LAI, FPAR) and meteorological/soil data, for enhanced prediction accuracy.
- To assess the performance of different ResNet50-LSTM configurations and feature combinations for optimal rice yield forecasting.
Main Methods
- A hybrid deep learning model (ResNet50-LSTM) was developed, integrating ResNet50 for feature extraction from satellite data and LSTM for time-series prediction.
- Multi-modal data, including MODIS satellite indices (EVI, LAI, FPAR), meteorological, and soil data, were collected and preprocessed using Google Earth Engine.
- Three LSTM model configurations with varying layer architectures were tested, and feature importance was analyzed to identify optimal predictors.
Main Results
- The ResNet50-LSTM model with two interconnected LSTM layers demonstrated superior prediction performance.
- The model achieved high accuracy with a selected feature set (EVI, FPAR, climate, and soil variables), yielding R² = 0.9903 and RMSE = 0.1854.
- The combination of EVI and FPAR was found to be particularly effective for rice yield prediction.
Conclusions
- The developed ResNet50-LSTM framework offers a robust and accurate method for forecasting rice yields.
- The study highlights the potential of utilizing publicly available multi-source data for global crop yield estimation.
- This approach can support informed agricultural decisions, enhance productivity, and contribute to food security.
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