基于RF-GSO的温室番茄自适应调光系统设计与试验
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摘要
为满足温室番茄光环境的自适应调控,设计了基于RF-GSO(随机森林-萤火虫)模型的温室番茄自适应调光系统,实现温室中温度、CO_2浓度、光照强度的实时采集,同时通过无线传感网络将信息传输到温室番茄自适应调光系统软件平台上,该平台可动态显示实时环境参数,并能实现补光灯远程调控。采用RF-GSO算法对温室内番茄理想光照强度进行动态计算,并将其与传感器实测光照强度间的差值作为调控参数,实现温室内番茄光环境的自适应调控。试验结果表明,系统检测的光照强度与温室调光目标值的决定系数R~2为0. 955,均方根误差为2. 168μmol/(m~2·s),系统丢包率为0. 417%,说明基于RF-GSO的温室番茄自适应调光系统运行稳定、可靠。
The suitable light environment could promote the photosynthetic rate of plants,increase the dry matter quality and then increase the fruit yield. In order to meet the adaptive regulation of greenhouse tomato light environment,a greenhouse tomato self-adaptive dimming system based on random forestglowworm swarm optimization algorithm( RF-GSO) model was designed to realize the real-time collection of temperature,CO_2 concentration and light intensity in greenhouse. At the same time,the information was transmitted to the software platform of greenhouse tomato self-adaptive dimming system through wireless sensor network. The platform could dynamically display the real-time ring. The ambient parameters could also realize the remote control of supplementary light. The ideal illumination intensity oftomato in greenhouse was calculated dynamically by RF-GSO algorithm,and the difference between the ideal illumination intensity and the measured illumination intensity of the sensor was taken as the control parameter to realize the adaptive control of the light environment of tomato in greenhouse. The experimental results showed that the determination coefficient R~2 between the illumination intensity detected by the system and the target value of greenhouse dimming was 0. 955,the root mean square error was 2. 168 μmol/( m~2·s),and the system packet loss rate was 0. 417%. It was showed that the adaptive dimming system of greenhouse tomato based on RF-GSO could achieve stable and reliable operation.
The suitable light environment could promote the photosynthetic rate of plants,increase the dry matter quality and then increase the fruit yield. In order to meet the adaptive regulation of greenhouse tomato light environment,a greenhouse tomato self-adaptive dimming system based on random forestglowworm swarm optimization algorithm( RF-GSO) model was designed to realize the real-time collection of temperature,CO_2 concentration and light intensity in greenhouse. At the same time,the information was transmitted to the software platform of greenhouse tomato self-adaptive dimming system through wireless sensor network. The platform could dynamically display the real-time ring. The ambient parameters could also realize the remote control of supplementary light. The ideal illumination intensity oftomato in greenhouse was calculated dynamically by RF-GSO algorithm,and the difference between the ideal illumination intensity and the measured illumination intensity of the sensor was taken as the control parameter to realize the adaptive control of the light environment of tomato in greenhouse. The experimental results showed that the determination coefficient R~2 between the illumination intensity detected by the system and the target value of greenhouse dimming was 0. 955,the root mean square error was 2. 168 μmol/( m~2·s),and the system packet loss rate was 0. 417%. It was showed that the adaptive dimming system of greenhouse tomato based on RF-GSO could achieve stable and reliable operation.
引文
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[2]路涛.亚高温强光胁迫下番茄幼苗光抑制及光保护机制研究[D].沈阳:沈阳农业大学,2016.LU Tao.Photoinhibition and photoprotection mechanism of tomato seedlings under sub-high temperature and high light stress[D].Shenyang:Shenyang Agricultural University,2016.(in Chinese)
[3]何静雯,明萌,卢丹,等.弱光胁迫对植物生理特性影响的研究进展[J].中国农学通报,2018,34(6):123-130.HE Jingwen,MING Meng,LU Dan,et al.Research progress on the effects of weak light stress on plant physiological characteristics[J].Chinese Agricultural Science Bulletin,2018,34(6):123-130.(in Chinese)
[4]胡瑾,闫柯,何东健,等.基于改进型鱼群算法的番茄光环境调控目标值模型[J].农业机械学报,2016,47(1):260-265.HU Jin,YAN Ke,HE Dongjian,et al.A model of tomato light environment regulation target value based on improved fish swarm algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(1):260-265.(in Chinese)
[5]辛萍萍,张珍,王智永,等.基于支持向量机-改进型鱼群算法的CO2优化调控模型[J].农业机械学报,2017,48(6):249-256.XIN Pingping,ZHANG Zhen,WANG Zhiyong,et al.CO2optimization control model based on support vector machine-improved fish swarm algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(6):249-256.(in Chinese)
[6]刘新英,殷鉴,李寒,等.CO2与氮肥交互作用的番茄光合速率预测模型[J].农业机械学报,2017,48(增刊):334-340.LIU Xinying,YIN Jian,LI Han,et al.The prediction model of tomato photosynthesis rate by interaction of CO2and nitrogen fertilizer[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(Supp.):334-340.(in Chinese)
[7]于海业,孔丽娟,刘爽,等.植物生产的光环境因子调控应用综述[J].农机化研究,2018,40(8):1-9.YU Haiye,KONG Lijuan,LIU Shuang,et al.A review of the application of light environmental factors in plant production[J].Journal of Agricultural Mechanization Research,2018,40(8):1-9.(in Chinese)
[8]SCHWEND T,BECK M,PRUCKER D,et al.Test of a PAR sensor-based,dynamic regulation of LED lighting in greenhouse cultivation of helianthus annuus[J].European Journal of Horticultural Science,2016,81(3):152-156.
[9]IERSEL M W V,WEAVER G,MARTIN M T,et al.A chlorophyll fluorescence-based biofeedback system to control photosynthetic lighting in controlled environment agriculture[J].Journal of the American Society for Horticultural Science,2016,141(2):169-176.
[10]唐宇,骆少明,黄伟锋,等.基于数控恒流技术的蓝莓光照调节器设计[J].农机化研究,2018,40(8):52-57,69.TANG Yu,LUO Shaoming,HUANG Weifeng,et al.Design of blueberry illumination regulator based on numerical control constant current technology[J].Journal of Agricultural Mechanization Research,2018,40(8):52-57,69.(in Chinese)
[11]刘翔.基于光温耦合的设施光环境检测与智能调控系统设计[D].杨凌:西北农林科技大学,2015.LIU Xiang.Design of facility light environment detection and intelligent control system based on light-temperature coupling[D].Yangling:Northwest A&F University,2015.(in Chinese)
[12]胡瑾,樊宏攀,张海辉,等.基于无线传感器网络的温室光环境调控系统设计[J].农业工程学报,2014,30(4):160-167.HU Jin,FAN Hongpan,ZHANG Haihui,et al.Design of greenhouse light environment control system based on wireless sensor network[J].Transactions of the CSAE,2014,30(4):160-167.(in Chinese)
[13]樊宏攀.基于多因子耦合的光环境调控系统研究与设计[D].杨凌:西北农林科技大学,2014.FAN Hongpan.Research and design of light environment control system based on multi-factor coupling[D].Yangling:Northwest A&F University,2014.(in Chinese)
[14]何小英.基于物联网的家庭节能控制系统研究[D].荆州:长江大学,2018.HE Xiaoying.Research on home energy-saving control system based on internet of things[D].Jingzhou:Yangtze University,2018.(in Chinese)
[15]MONTEIRO F R,GARCIA M A P,CORDEIRO L C,et al.Bounded model checking of C++programs based on the Qt cross-platform framework[J].Software Testing,Verification and Reliability,2017,27(3):1-16.
[16]王升,付智勇,陈洪松,等.基于随机森林算法的参考作物蒸发蒸腾量模拟计算[J].农业机械学报,2017,48(3):302-309.WANG Sheng,FU Zhiyong,CHEN Hongsong,et al.Simulation calculation of reference crop evapotranspiration based on random forest algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(3):302-309.(in Chinese)
[17]YANG Renmin,ZHANG Ganlin,LIU Feng,et al.Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem[J].Ecological Indicators,2016,60(1):870-878.
[18]邹东尧,刘碧微,杨威.基于飞行理论萤火虫算法的TDOA与GROA定位机制[J].计算机应用研究,2017,34(9):2768-2772.ZOU Dongyao,LIU Biwei,YANG Wei.TDOA and GROA positioning mechanism based on flight theory firefly algorithm[J].Journal of Computer Applications,2017,34(9):2768-2772.(in Chinese)
[19]MARINAKI M,MARINAKIS Y.A glowworm swarm optimization algorithm for the vehicle routing problem with stochastic demands[J].Expert Systems with Applications,2016,46(10):145-163.
[20]丁兆堂,卢育华,徐坤.环境因子对番茄光合特性的影响[J].山东农业大学学报(自然科学版),2003,34(3):356-360.DING Zhaotang,LU Yuhua,XU Kun.Effects of environmental factors on photosynthetic characteristics of tomato[J].Journal of Shandong Agricultural University(Natural Science Edition),2003,34(3):356-360.(in Chinese)