Research on Sensor-based Agricultural Greenhouse Data Direct Reporting System and Intelligent Control
The rapid development of agricultural internet of things,big data,artificial intelligence and other technologies have provided strong support for data collection,analysis and regulation of greenhouse vegetable production.In order to meet the requirements of intelligent direct reporting scenarios for greenhouse environmental data,this study developed the deployment specifications of greenhouse environmental sensors.3 greenhouse control temperature prediction models based on LSTM,CNN-LSTM,and CNN-LSTM-Attention were designed and compared.Among them,CNN-LSTM-Attention prediction model had the best performance,with MSE,MAE and R2 of 0.457 0,0.319 5 and 0.987 3,respectively.The ARIMA-based sensor data error correction method was designed and the difference between the predicted soil moisture data and the actual measurement was not significant.The parameter threshold model of environmental information of common greenhouse fruit and vegetable crops was integrated,and the mobile end of greenhouse data direct reporting and intelligent regulation system was developed.Thus,it could guide the standardized deployment of greenhouse environmental sensors,temperature prediction and error correction,and auxiliary decision-making for common greenhouse fruit and vegetable cultivation.Above results provided technical means for data collection,business analysis,and greenhouse control in greenhouse data direct reporting scenarios,and contributed to the high-quality development of the smart greenhouse vegetable industry.
sensorsdata direct reportinggreenhouse application scenariotemperature predictiondata error correction
国家科技期刊平台
NSTL
万方数据
