AI-driven insights into rural industry dynamics: a data-driven approach

Rural industries are essential to local economies and cultural preservation but face infrastructure gaps, volatile markets, and resource inefficiencies. This study explores how AI-driven insights can address these challenges through a data-driven approach. A multi-source dataset, comprising government reports, market data, and stakeholder interviews, was analysed using advanced machine learning methods: LSTM for time-series forecasting, transformers for text analysis, and GNNs for supply chain mapping. Ensemble models outperformed individual ones, with an F1-score of 0.95 and RMSE reduced to 9.20. SHAP-based explainability revealed key factors influencing outcomes, including marketing expenditure, environmental variables, and consumer demand. The findings show that AI can enhance decision-making, resource use, and sustainable development in rural sectors. Ethical concerns and algorithmic biases were also addressed to ensure fair and inclusive results. This study demonstrates AI's transformative potential in rural contexts and underscores the importance of tailoring models to specific socio-economic environments for maximum impact.