Forecasting trend of agricultural talents flow by spatio-temporal graph neural network and LightGBM

Key Points

• Forecasting talent flow in agriculture can improve workforce distribution, enhancing overall productivity.
• Key evidence includes the use of spatio-temporal graph neural networks, which models complex relationships effectively.
• Assessment uses graph neural networks and LightGBM, combining advanced algorithms for accurate predictions.
• Highlights the importance of timely data in agricultural sectors, emphasizing potential enhancements in resource allocation.