The prediction of collective Economic development based on the PSO-LSTM model in smart agriculture

Abstract

Smart agriculture can promote the rural collective economy’s resource coordination and market access through the Internet of Things and artificial intelligence technology and guarantee the collective economy’s high-quality, sustainable development. The collective agricultural economy (CAE) is non-linear and uncertain due to regional weather, policy and other reasons. The traditional statistical regression model has low prediction accuracy and weak generalization ability on such issues. This article proposes a production prediction method using the particle swarm optimization-long short term memory (PSO-LSTM) model to predict CAE. Specifically, the LSTM method in the deep recurrent neural network is applied to predict the regional CAE. The PSO algorithm is utilized to optimize the model to improve global accuracy. The experimental results demonstrate that the PSO-LSTM method performs better than LSTM without parameter optimization and the traditional machine learning methods by comparing the RMSE and MAE evaluation index. This proves that the proposed model can provide detailed data references for the development of CAE.

Keywords

• Smart agriculture
• LSTM
• Production prediction
• Particle swarm optimization
• Recurrent neural network