Neural Network-Based Agricultural Economic Behavior Pattern Recognition: From Big Data to Intelligent Decision-Making

Many factors shape agricultural economic behaviour, but large datasets on crop yields, market price volatilities and climate change — perhaps too much data, in fact — are the most prominent In this regard, the development of models that accurately identify and predict agricultural economic behaviour, is quite topical. In this paper, a pattern recognition method for agricultural economic behaviour based on Long Short-Term Memory Network (LSTM) is presented, its performance is improved through multiple optimisation methods. In prior work, LSTM networks have demonstrated an outstanding performance in processing time series data, making them appropriate to capture dynamic features such as seasonal variations, cyclical patterns, and market trends in agricultural economic activities. Finally, to improve the model performance, this paper combines data preprocessing methods, Dropout regularization technology, and PSO algorithm for hyperparameter tuning. Such optimising features have proven effectively increasing model capacity to find latent-augmented patterns in agriculture econ space and provide more robust and accurate prediction results. Experimental results show that the model proposed in this paper achieves a significant accuracy improvement over traditional methods for agricultural economic forecasting tasks.