A Data Factor Study for Machine Learning on Heterogenous Edge Computing

被引:3
作者
Chang, Dong-Meau [1 ]
Hsu, Tse-Chuan [2 ]
Yang, Chao-Tung [3 ,4 ]
Yang, Junjie [1 ]
机构
[1] Lingnan Normal Univ, Sch Comp Sci & Intelligence Educ, Zhanjiang 524048, Peoples R China
[2] Soochow Univ, Dept Comp Sci & Informat Management, Taipei 10048, Taiwan
[3] Tunghai Univ, Dept Comp Sci, Taichung 407224, Taiwan
[4] Tunghai Univ, Res Ctr Smart Sustainable Circular Econ, Taichung 407224, Taiwan
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 06期
关键词
intelligent agriculture; data modeling; machine learning; edge computing; factor selection analysis;
D O I
10.3390/app13063405
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
As plants and animals grow, there are many factors that influence the changes that will affect how plants grow and how botanical experts distinguish them. The use of the Internet of Things (IoT) for data collection is an important part of smart agriculture. Many related studies have shown that remote data management and cloud computing make it possible and practical to monitor the functionality of IoT devices. In automated agriculture, machine learning intelligence is more necessary to use to automatically determine whether the correlation between learning factors influences plant growth patterns. In this research experiment, the relevant data are automatically collected through a detection device, and data modeling and computation are performed in an edge computing environment. At the same time, the data model is transmitted via the communication protocol, and another node is available for verification of the modeling and calculation results. The experimental results show that the single-point data-trained model is able to accurately predict the growth trend of the plants. In the case of verification of the second measurement point at a different space, the data model must be trained with more than two layers in order to improve the training results and reduce errors.
引用
收藏
页数:14
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