Content area
Currently, data collection from a single sensor is no longer sufficient to meet people's information needs. To accurately predict the actual temperature, humidity, and lighting conditions in greenhouse environments and have a positive effect on vegetable cultivation, this study proposes a radial basis function neural network optimized by cuckoo search algorithm, and combines the optimized Dempster Shafer theory for greenhouse environment prediction. The optimized radial basis function of the improved cuckoo search algorithm converged at 10 iterations, and the recall rate finally converged to around 0.9. The optimized radial basis function of the improved cuckoo search algorithm was at the minimum level among the three error values, with an average reduction of 0.14, 0.25, and 0.24 compared to the other two algorithms. The humidity was reduced by an average of 0.25, 0.49, and 0.39, and the lighting was reduced by an average of 3, 27, and 2. After introducing the improved Dempster Shafer theory in the second example, the uncertainty of the final result decreased from 32.3% to 23.9%, while the output probability increased from 11.3% to 68.5%. Therefore, the radial basis function optimized by the improved cuckoo search algorithm has better prediction accuracy for various indicators in the greenhouse, while the error is small, which can significantly reduce uncertainty. This study provides a theoretical basis for the layout of greenhouse environmental monitoring equipment in the vegetable production process.