摘要
研究了一种基于多数据融合+BP神经网络的农业温室大棚监控系统。在大棚内部设置多点传感器,避免单个传感器测量不准确的问题,通过加权平均算法进行数据融合,再结合BP神经网络对参数的变化趋势进行判断,最终得到决策后的控制策略。本研究实现了更为精准的大棚环境参数预测,多数据融合后的参数更准确,再进行神经网络的训练,获得各参数变化的趋势,为农业温室大棚提供良好的植株生长环境。
A monitoring system of agricultural greenhouse based on Multi-data fusion+BP neural network was studied.A Multi-point sensor was set inside the greenhouse to avoid the problem of inaccurate measurement of a single sensor.The weighted average algorithm was used for data fusion,and the BP neural network was combined to judge the changing trend of parameters.Finally,the control strategy after decision was obtained.This study had achieved a more accurate prediction of greenhouse environmental parameters,and the parameters after Multi-data fusion were more accurate.Then,neural network training was carried out to obtain the trend of parameter changes,so as to provide a good plant growth environment for agricultural greenhouse.
作者
许德立
皇甫森森
李澍源
XU De-li;HUANGFU Sen-sen;LI Shu-yuan(Jinshan College of Fujian Agriculture and Forestry University,Fuzhou350002,China;Jiageng College,XiamenUniversity,Zhangzhou 363105,Fujian,China;Logistics Management Office of Fujian Normal University,Fuzhou350117,China)
出处
《湖北农业科学》
2023年第1期167-171,176,共6页
Hubei Agricultural Sciences
基金
福建省教育厅中青年教师教育科研项目(JAT191134)。
