基于卡尔曼滤波器和多层感知器的大麦幼苗最优生长参数预测

doi:10.11959/j.issn.2096-3750.2021.00218

Abstract

Abstract:

In order to improve the quality and planting efficiency of barley seedlings in the growth chamber, the Kalman filter algorithm was firstly used to process the data collected by the sensor, which effectively reduced the influence of environmental factors and the error of the sensor itself, improved the accuracy of the collected data, and ensured the precise control in the growth chamber and accurate test data.Then multiple nonlinear regression, radial basis function and multilayer perceptron neural network were used to analyze the average growth height, seedling weight and seed weight of barley seeds about 160 hours after germination under different conditions.The drying ratio was analyzed and compared.The results show that the multi-layer perceptron network model fits the data best.Using this model to predict the average height of barley seedlings and the ratio of seedling weight of barley seedlings in the optimal environment is basically consistent with the actual planting effect, which provides a certain reference for the planting of barley seedlings in the growth chamber.

Key words: barley seedling germination, Kalman filter, radial basis function, multilayer perceptron, sensor

CLC Number:

TP271

Yunlong HUANG, Zhengquan LI, Yujia SUN. Prediction of optimal growth parameters of barley seedling based on Kalman filter and multilayer perceptron[J]. Chinese Journal on Internet of Things, 2021, 5(4): 90-98.

Figures/Tables 6

References 26

[1]	梅玉芹, 俸继红, 杨建菊 ,等. 温度和盐对大麦种子萌发及幼苗生长的影响[J]. 种子世界, 2014(2): 36-37.
	MEI Y Q , FENG J H , YANG J J ,et al. Effects of temperature and salt on Seed Germination and seedling growth of barley[J]. Seed World, 2014(2): 36-37.
[2]	张敬云, 谢作如 . 利用 Arduino 探究种子萌发的最宜土壤湿度[J]. 中国信息技术教育, 2016(Z2): 89-91.
	ZHANG J Y , XIE Z R . Using Arduino to explore the optimal soil moisture for seed germination[J]. China Information Technology Edu-cation, 2016(Z2): 89-91.
[3]	WU Y B , YAN Z M , WU D F ,et al. Effect of NaCl stress on seed germination and seedling growth in wheat[C]// 2010 4th International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2010: 1-3.
[4]	YI Z H , CUI J J , FU Y M ,et al. Effect of different light intensity on physiology,antioxidant capacity and photosynthetic characteristics on wheat seedlings under high CO2 concentration in a closed artificial ecosystem[J]. Photosynthesis Research, 2020,144(1): 23-34.
[5]	DOEHLERT D C , KU M S B , EDWARDS G E . Dependence of the post-illumination burst of CO2 on temperature,light,CO2,and O2 concentration in wheat (triticum aestivum)[J]. Physiologia Plantarum, 1979,46(4): 299-306.
[6]	武闯 . 仓库温湿度监控系统实现及测点定位精度研究[D]. 芜湖:安徽师范大学, 2016.
	WU C . Research on the realization of warehouse temperature and hu-midity monitoring system and the positioning accuracy of measuring points[D]. Wuhu:Anhui Normal University, 2016.
[7]	赵国强, 贾鹤鸣, 张森 ,等. 微型植物工厂温湿度场分析与传感器优化布局[J]. 森林工程, 2019,35(2): 61-68.
	ZHAO G Q , JIA H M , ZHANG S ,et al. Analysis of temperature and humidity field and layout optimization of sensor in micro plant facto-ry[J]. Forest Engineering, 2019,35(2): 61-68.
[8]	弓倩 . 基于IMM 的多传感器融合跟踪算法研究[D]. 西安:西安工业大学, 2012.
	GONG Q . A research on tracking algorithm of multi-sensor data fusion based on interactive multiple models (IMM)[D]. Xi'an:Xi'an Tech-nological University, 2012.
[9]	李海艳, 李维嘉, 黄运保 . 基于卡尔曼滤波的多传感器测量数据融合[J]. 武汉大学学报(工学版), 2011,44(4): 521-525,529.
	LI H Y , LI W J , HUANG Y B . Multisensor measured data fusion based on Kalman filtering[J]. Engineering Journal of Wuhan University, 2011,44(4): 521-525,529.
[10]	KOVVALI N , BANAVAR M , SPANIAS A . An introduction to Kalman filtering with MATLAB examples[C]// Synthesis Lectures on Signal Processing. Morgan ＆ Claypool, 2013: 79-81.
[11]	彭丁聪 . 卡尔曼滤波的基本原理及应用[J]. 软件导刊, 2009,8(11): 32-34.
	PENG D C . Basic principle and application of Kalman filter[J]. Soft-ware Guide, 2009,8(11): 32-34.
[12]	张谦, 景占荣 . 一种过程噪声自适应调节的卡尔曼滤波算法[J]. 电子测量技术, 2007,30(5): 18-20,31.
	ZHANG Q , JING Z R . Kalman filtering algorithm with process noise adjusting adaptively[J]. Electronic Measurement Technology, 2007,30(5): 18-20,31.
[13]	韩璐, 景占荣, 段哲民 . 一种基于预测滤波器的自适应卡尔曼滤波算法[J]. 火力与指挥控制, 2010,35(3): 110-113.
	HAN L , JING Z R , DUAN Z M . Application of adaptive Kalman filter to missile fixed SINS/GPS integrated navigation system based on pre-dictive filtering[J]. Fire Control ＆ Command Control, 2010,35(3): 110-113.
[14]	曾康华 . 计量经济学[M]. 北京: 清华大学出版社, 2016.
	ZENG K H . Econometrics[M]. Beijing: Tsinghua University Press, 2016.
[15]	赵坷 . 非线性模型解算及其应用研究[D]. 青岛:山东科技大学, 2014.
	ZHAO K . Nonlinear model solution and its application[D]. Qingdao:Shandong University of Science and Technology, 2014.
[16]	石国春 . 关于序列二次规划(SQP)算法求解非线性规划问题的研究[D]. 兰州:兰州大学, 2009.
	SHI G C . Research on algorithm of sequential quadratic programming for nonlinear programming problems[D]. Lanzhou:Lanzhou Univer-sity, 2009.
[17]	CHERRAK O , GHENNIOUI H , ABARKAN E ,et al. Levenberg-Marquardt algorithm[J]. Tutorial on LM Algorithm, 2004,11(1): 101-110.
[18]	何叶丹, 马昌凤, 范斌 . 求解非线性方程组的一种新的L-M方法[J]. 福建师范大学学报(自然科学版), 2014,30(2): 20-25,41.
	HE Y D , MA C F , FAN B . A new L-M method for the systems of non-linear equalities[J]. Journal of Fujian Normal University (Natural Science Edition), 2014,30(2): 20-25,41.
[19]	李艳 . 径向基函数及其应用[D]. 大连:大连理工大学, 2009.
	LI Y . Radial basis function and its application[D]. Dalian:Dalian Uni-versity of Technology, 2009.
[20]	朱明星, 张德龙 . RBF网络基函数中心选取算法的研究[J]. 安徽大学学报(自然科学版), 2000,24(1): 72-78.
	ZHU M X , ZHANG D L . Study on the algorithms of selecting the radial basis function center[J]. Journal of Anhui University (Natural Sciences), 2000,24(1): 72-78.
[21]	潘琪 . 径向基神经网络基函数中心确定方法改进研究[D]. 哈尔滨:东北农业大学, 2017.
	PAN Q . Research on establishing an improved method to determine the radial basis function center of radial basis function neural net-work[D]. Harbin:Northeast Agricultural University, 2017.
[22]	DU Y L , . Prediction of consumer price index based on RBF neural network[C]// 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018: 2858-2861.
[23]	ROSLI N , IBRAHIM R , ISMAIL I ,et al. Neural network architecture selection for efficient prediction model of gas metering system[C]// 2016 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA). IEEE, 2016: 1-5.
[24]	KASHANINEJAD M , DEHGHANI A A , KASHIRI M . Modeling of wheat soaking using two artificial neural networks (MLP and RBF)[J]. Journal of Food Engineering, 2009,91(4): 602-607.
[25]	NANDI A , JANA N D , DAS S . Improving the performance of neural networks with an ensemble of activation functions[C]// 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020: 1-7.
[26]	IZZELDIN H , ASIRVADAM V S , SAAD N . Enhanced conjugate gradient methods for training MLP-networks[C]// 2010 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2010: 139-143.

Metrics

Recommended 0

No Suggested Reading articles found!

误差参数		麦苗平均高度		麦苗种子重量干燥比
误差参数		均方误差MSE	平均相对误差MRE	均方误差MSE	平均相对误差MRE
	NLR	0.004 8	0.003 8	0.005 7	0.009 3
训练集	RBF	0.003 2	0.001 6	0.009 2	0.010 5
	MLP	0.004 6	0.003 8	0.004 7	0.008 9
	NLR	0.004 7	0.004 1	0.018 6	0.016 0
测试集	RBF	0.011 2	0.003 6	0.009 6	0.011 4
	MLP	0.004 9	0.003 7	0.005 8	0.010 1

Prediction of optimal growth parameters of barley seedling based on Kalman filter and multilayer perceptron

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 26

Related Articles 15

Metrics

Recommended 0

[1]	Chenchen ZHANG, Xumin GAO, Ziqi YE, Mingming BO, Zefeng HU, Yongjin WANG. Pulse detection sensor with green light transceiver integrated optoelectronic chip [J]. Chinese Journal on Internet of Things, 2023, 7(2): 43-49.
[2]	Jing WU, Sheng LI, Jing ZHANG, Ming XIN, Ruowen TAO, Zhou ZHOU, Lijia PAN, Yi SHI. New flexible sensor for the internet of things [J]. Chinese Journal on Internet of Things, 2023, 7(2): 1-14.
[3]	Fei TONG, Rucong SUI, Yu CHEN, Heng SU, Hengrui LIU, Shangfeng SU, Yuke YAN. An energy-efficient multi-channel MAC protocol for linear sensor network [J]. Chinese Journal on Internet of Things, 2022, 6(4): 27-40.
[4]	Kailei ZHU, Aijing SUN. WSN clustering routing algorithm based on Cuckoo Search algorithm optimized K-means [J]. Chinese Journal on Internet of Things, 2022, 6(1): 73-81.
[5]	Xin SU, Su JIANG, Yiqing ZHOU. Research on location privacy protection of mobile terminals for maritime monitoring sensor networks [J]. Chinese Journal on Internet of Things, 2021, 5(4): 26-36.
[6]	Xiaoqiang ZHAO, Shuai WU, Chuanyi GAO, Ning LI, Bodong LI, Xiaoyong YANG. Research on DV-Hop location algorithm based on range correction and improved gray wolf optimizer [J]. Chinese Journal on Internet of Things, 2021, 5(4): 62-70.
[7]	Xiaoqiang ZHAO, Shaoya REN, Yongzhi ZHAI, Heng QUAN, Ting YANG. Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer [J]. Chinese Journal on Internet of Things, 2021, 5(2): 97-106.
[8]	Xin SU, Ziyi WANG, Yupeng WANG, Siyuan ZHOU. Multi-access edge computing offloading in maritime monitoring sensor networks [J]. Chinese Journal on Internet of Things, 2021, 5(1): 36-52.
[9]	Jinfeng YANG,Ji WANG. Research of precision agriculture WSN protocols [J]. Chinese Journal on Internet of Things, 2020, 4(4): 62-69.
[10]	Fuyang CHEN,Bin JIANG,Yu SHA. Railway safety monitoring algorithm based on distributed optical fiber vibration sensor [J]. Chinese Journal on Internet of Things, 2020, 4(3): 106-111.
[11]	Yongsheng SANG,Renhao LI,Yaoqian LI,Qiangwei WANG,Yao MAO. Research on neuromorphic vision sensor and its applications [J]. Chinese Journal on Internet of Things, 2019, 3(4): 63-71.
[12]	Jie HU,Jie ZHAN,Baoquan REN. K connectivity algorithm based on flow network in wireless sensor networks [J]. Chinese Journal on Internet of Things, 2019, 3(3): 11-17.
[13]	Jin CHEN,Xiwei HUANG,Yangbo LI,Jixuan LIU,Lingling SUN. Miniaturized lensless microfluidic on-chip bio-imaging detection [J]. Chinese Journal on Internet of Things, 2019, 3(2): 9-19.
[14]	Pengju HE,Gangyi LIU,Siyi LIU. Research on multi-target recognition method based on WSN and blind source separation [J]. Chinese Journal on Internet of Things, 2019, 3(1): 45-50.
[15]	Chen YU,Lijuan ZHANG,Hai JIN. Research progress and trend of big data-driven intelligent transportation system [J]. Chinese Journal on Internet of Things, 2018, 2(1): 56-63.