干旱区生态环境知识资源中心(Arid): IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN

Arid

DOI	10.1016/j.iot.2023.100829
	IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN
	Barriga, Arturo; Barriga, Jose A.; Monino, Maria Jose; Clemente, Pedro J.
通讯作者	Barriga, A
来源期刊	INTERNET OF THINGS
ISSN	2543-1536
EISSN	2542-6605
出版年	2023
卷号	23
英文摘要	Industry 4.0 involves the digital transformation of industrial sectors. Given the current climate change scenario and the scarcity of water in semi-arid regions, this digital transformation has to take into account the sustainable use of water. In agriculture, one of the most water-intensive sectors, to optimise the use of water, precision irrigation techniques are being applied. As a result of the digital transformation of agriculture, a key aspect for the application of these precision irrigation techniques, the crop water stress, can be predicted from a Wireless Sensor Network (WSN) of leaf-turgor pressure sensors. However, these sensors often fail, introducing errors in the data, which could lead to inaccurate application of precision irrigation techniques compromising crops and yields. So, sensor fault identification is a must. Nevertheless, sensor fault identification is a tedious and costly task that requires an expert to manually review all sensors and each of their measurements over the last 24 h. In this work, with the aim of digitally transforming this task, an IoT-based expert system is proposed. By means of a novel learning model, this system is capable of identifying sensor faults with 84.2% f1-score and 0.94 AUC ROC. Note that to train this learning model, only real-world data gathered from an experimental plot has been used. In addition, the real-world application of the IoT-based expert system in this plot is shown and discussed. Furthermore, a novel methodology that summarises the main findings and techniques applied in this study is also illustrated.
英文关键词	Internet of things Leaf-turgor pressure sensors Machine learning Precision agriculture Sensor faults Regional Development Fund (ERDF).
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001056579100001
WOS关键词	EXTREME LEARNING-MACHINE ; SENSOR ; INTERNET ; NETWORK ; THINGS ; IMPACT
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397058
推荐引用方式 GB/T 7714	Barriga, Arturo,Barriga, Jose A.,Monino, Maria Jose,et al. IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN[J],2023,23.
APA	Barriga, Arturo,Barriga, Jose A.,Monino, Maria Jose,&Clemente, Pedro J..(2023).IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN.INTERNET OF THINGS,23.
MLA	Barriga, Arturo,et al."IoT-based expert system for fault detection in Japanese Plum leaf-turgor pressure WSN".INTERNET OF THINGS 23(2023).