Cloud-based configurable data stream processing architecture in rural economic development.

Area of Science:

• Agricultural Economics
• Data Science
• Machine Learning

Background:

• Traditional data processing methods struggle with the complexity of agricultural price prediction.
• Accurate price forecasting is crucial for rural informatization, agricultural efficiency, and economic growth.

Purpose of the Study:

• To develop an advanced framework for predicting agricultural product prices.
• To overcome the limitations of existing data processing techniques in this domain.

Main Methods:

• The study proposes the Reinforcement Learning-Convolutional Neural Network-Gated Recurrent Unit (RL-CNN-GRU) framework.
• This model integrates 1D-CNN for feature extraction and GRUs for temporal pattern recognition.
• Reinforcement learning is employed to optimize the model's accuracy with multidimensional time series data.

Main Results:

• The RL-CNN-GRU framework demonstrated superior performance compared to traditional models.
• The proposed framework achieved lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) in price predictions.
• Validation was conducted using both public and proprietary agricultural datasets.

Conclusions:

• The RL-CNN-GRU framework offers a more accurate tool for agricultural price prediction.
• This advancement supports improved decision-making in agricultural processes and contributes to rural economic development.
• The study highlights the potential of integrated machine learning models for enhancing rural informatization.