湖北农业科学 ›› 2023, Vol. 62 ›› Issue (1): 167-171.doi: 10.14088/j.cnki.issn0439-8114.2023.01.029

• 农业工程 • 上一篇    下一篇

基于多数据融合+BP神经网络的农业温室大棚环境监控系统的研究

许德立1, 皇甫森森2, 李澍源3   

  1. 1.福建农林大学金山学院,福州 350002;
    2.厦门大学嘉庚学院,福建 漳州 363105;
    3.福建师范大学后勤管理处,福州 350117
  • 收稿日期:2022-09-22 出版日期:2023-01-25 发布日期:2023-03-07
  • 通讯作者: 李澍源(1991-),男,福建福州人,助理工程师,主要从事集成电路、自动控制系统设计和农业智能控制研究,(电话)13635289770(电子信箱)lisy5@foxmail.com。
  • 作者简介:许德立(1998-),男,广西玉林人,在读本科生,研究方向为农业工程、工程管理,(电话)13950400526(电子信箱)45872535@qq.com
  • 基金资助:
    福建省教育厅中青年教师教育科研项目(JAT191134)

Research on environment monitoring system of agricultural greenhouse based on Multi-data fusion+BP neural network

XU De-li1, HUANGFU Sen-sen2, LI Shu-yuan3   

  1. 1. Jinshan College of Fujian Agriculture and Forestry University,Fuzhou 350002,China;
    2. Jiageng College,Xiamen University,Zhangzhou 363105, Fujian,China;
    3. Logistics Management Office of Fujian Normal University,Fuzhou 350117, China
  • Received:2022-09-22 Online:2023-01-25 Published:2023-03-07

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。

关键词: 温室大棚, BP神经网络, 多数据融合, 加权平均

Abstract: 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.

Key words: greenhouse, BP neural network, Multi-data fusion, weighted average

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