Research on real-time ginseng life cycle data collection and analysis system based on Internet of Things technology

In this study, a crop growth prediction system based on LSTM, SVR and their hybrid models is proposed and combined with particle swarm optimization (PSO) algorithm to optimize the model hyperparameters. The completeness and accuracy of the data is ensured by preprocessing the sensor data, removing the noise using Kalman filtering, and filling the missing data by linear interpolation. In the growth prediction model, LSTM can effectively capture the temporal pattern of crop growth, while SVR is good at dealing with regression problems, and the hybrid model of LSTM and SVR combines the advantages of both, with faster convergence and higher prediction accuracy. The PSO optimization algorithm effectively adjusts the hyperparameters of the model, which significantly improves the prediction accuracy and stability of the model. The experimental results show that the PSO-optimized hybrid model exhibits strong generalization ability and efficient prediction performance in crop growth prediction. This study provides a new approach for intelligent management of agriculture and a theoretical basis for future system optimization and cross-crop prediction.