摘要
农业物联网、大数据、人工智能等技术的飞速发展为温室蔬菜生产的数据采集、分析、调控提供了有力支撑。为满足温室环境数据智能直报场景需求,制定了温室环境传感器的部署规范,设计并对比分析了基于LSTM、CNN-LSTM及CNN-LSTM-Attention的3种温室调控温度预测模型。基于CNN-LSTM-Attention预测模型的性能最好,其MSE、MAE、R^(2)分别为0.4570、0.3195和0.9873。设计了基于ARIMA的温室传感器数据纠错方法,实现土壤湿度预测数据值与实际测量差异不显著。整合了常见温室果蔬种植作物对环境信息的参数阈值模型,并开发了温室数据直报与智能调控系统移动端,能够指导温室环境传感器的规范部署、温度预测与纠错及常见温室果蔬种植辅助决策。研究结果为温室数据直报场景的数据采集、业务分析、温室调控提供技术手段,有助于智慧温室蔬菜产业高质量发展。
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 R^(2)of 0.4570,0.3195 and 0.9873,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.
作者
熊晓菲
吴文茜
霍洪彦
张馨
于艳
安冬
张同
吴建伟
XIONG Xiaofei;WU Wenqian;HUO Hongyan;ZHANG Xin;YU Yan;AN Dong;ZHANG Tong;WU Jianwei(Information Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100971,China;Beijing PAIDE Science and Technology Development Co.,Ltd.,Beijing 100097,China;Intelligent Equipment Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;Shanghai Longken Information Technology Co.,Ltd.,Shanghai 20094,China;College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;School of Computer and Control Engineering,Yantai University,Shandong Yantai 264005,China)
出处
《中国农业科技导报》
CAS
CSCD
北大核心
2024年第7期93-102,共10页
Journal of Agricultural Science and Technology
基金
上海市科技兴农项目(沪农科推字2022第3-2号)
北京市农林科学院项目(JJP2023-04,PT2023-30)
北京市科技计划项目(Z221100005822014)。
关键词
传感器
数据直报
温室应用场景
温度预测
数据纠错
sensors
data direct reporting
greenhouse application scenario
temperature prediction
data error correction
