Research on Multi environmental Parameter Prediction Model of Solar Greenhouse Based on the Whole Growth Period of Tomato
Year: 2025
Type: conference-paper
Abstract: Based on the potential relationships and temporal characteristics among four types of environmental parameters in the tomato production environment of solar greenhouses, three multi-environment parameter prediction models were constructed using the environmental dataset of the whole tomato growth period as model inputs: (1)The 3DCNN-CLSTM model was constructed by integrating the 3D Convolutional Neural Network (3DCNN) and Convolutional Long Short-Term Memory (CLSTM) network; (2)The PSO-3DCNN-CLSTM model was constructed by introducing the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the fully connected layer and the learning rate of the 3DCNN-CLSTM model; (3)The PSO-Attention-3DCNN-CLSTM model was constructed by incorporating an Attention mechanism layer into the PSO-3DCNN-CLSTM model, enabling the model to focus on key features. Through analysis and comparison of the prediction performances of the three models, the results show that the PSO-Attention-3DCNN-CLSTM model has the highest prediction accuracy.
Author: Feng Wang
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDGs: Zero hunger, Climate action
Open Access status: hybrid
OpenAlex ID: W4414288755