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

黄云龙; 李正权; 孙煜嘉

doi:10.11959/j.issn.2096-3750.2021.00218

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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

理论与技术 | 更新时间：2024-06-05

- 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
- Prediction of optimal growth parameters of barley seedling based on Kalman filter and multilayer perceptron
- 物联网学报 2021年5卷第4期页码：90-98
- 作者机构：
- 作者简介：
  
  [ "黄云龙（1996− ），男，江南大学物联网工程学院硕士生，主要研究方向为传感器智能设备应用" ]
  [ "李正权（1976− ），男，江南大学物联网工程学院教授，主要研究方向为大规模MIMO技术、协作通信、物联网等" ]
  [ "孙煜嘉（1995− ），男，江南大学物联网工程学院硕士生，主要研究方向为计算机视觉处理" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61571108);无锡市科技发展资金项目;The Wuxi Science and Technology Devel-opment Fund(H20191001);无锡市科技发展资金项目;The Wuxi Science and Technology Devel-opment Fund(G20192010)
- DOI：10.11959/j.issn.2096-3750.2021.00218
  中图分类号： TP271
- 纸质出版日期：2021-12-30，
  
  网络出版日期：2021-12，
- 稿件说明：
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黄云龙, 李正权, 孙煜嘉. 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[J]. 物联网学报, 2021,5(4):90-98.

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.
黄云龙, 李正权, 孙煜嘉. 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[J]. 物联网学报, 2021,5(4):90-98. DOI： 10.11959/j.issn.2096-3750.2021.00218.

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. DOI： 10.11959/j.issn.2096-3750.2021.00218.

摘要

为了提高生长舱大麦幼苗的质量和种植效率，首先利用卡尔曼滤波算法对传感器采集的数据进行处理，有效降低了环境因素和传感器本身误差的影响，提高了采集数据的精度，保证生长舱的精确控制和准确的实验数据，然后利用多元非线性回归、径向基函数和多层感知器神经网络对不同条件下，大麦种子萌发生长约160 h后的平均生长高度、麦苗重量和种子重量干燥比进行分析比较，结果表明，多层感知器网络模型对数据的拟合效果最好。利用该模型预测最优环境时的大麦幼苗平均高度和麦苗种子重量比与实际种植效果基本一致，为生长舱大麦幼苗的种植提供一定参考。

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.

关键词

麦苗萌发卡尔曼滤波径向基函数多层感知器传感器

Keywords

barley seedling germinationKalman filterradial basis functionmultilayer perceptronsensor

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