Nanosensors for plant health monitoring

2022 ◽  
pp. 449-461
Author(s):  
Suchit A. John ◽  
Amit Chattree ◽  
Pramod W. Ramteke ◽  
P. Shanthy ◽  
Tuan Anh Nguyen ◽  
...  
2021 ◽  
pp. 2106475
Author(s):  
Giwon Lee ◽  
Qingshan Wei ◽  
Yong Zhu

2005 ◽  
Vol 127 (08) ◽  
pp. 28-29
Author(s):  
Paul Sharke

This article focuses on the process involved in machinery health monitoring. Operators need know only about certain things for which they can take immediate action: a cavitating pump, an overheating motor, a severely vibrating train, a failing bearing. They do not need knowledge of longer-term concerns like imbalance or misalignment, the domain of maintenance gurus. Likewise, management needs information about overall plant health and the priorities of various repairs, but it does not want to know that a circulation water pump is exhibiting mild axial misalignment or another pump elsewhere is beginning to cavitate.


Author(s):  
Mohammad Hanan Bhat

: Plant health monitoring has been a significant field of research since a very long time. The scope of this research work conducted lies in the vast domain of plant pathology with its applications extending in the field of agriculture production monitoring to forest health monitoring. It deals with the data collection techniques based on IOT, pre-processing and post-processing of Image dataset and identification of disease using deep learning model. Therefore, providing a multi-modal end-to-end approach for plant health monitoring. This paper reviews the various methods used for monitoring plant health remotely in a non-invasive manner. An end-to-end low cost framework has been proposed for monitoring plant health by using IOT based data collection methods and cloud computing for a single-point-of-contact for the data storage and processing. The cloud agent gateway connects the devices and collects the data from sensors to ensure a single source of truth. Further, the deep learning computational infrastructure provided by the public cloud infrastructure is exploited to train the image dataset and derive the plant health status


2020 ◽  
Vol 29 (5) ◽  
pp. 755-761
Author(s):  
Sila Deniz Calisgan ◽  
Vageeswar Rajaram ◽  
Sungho Kang ◽  
Antea Risso ◽  
Zhenyun Qian ◽  
...  

2019 ◽  
Vol 10 ◽  
Author(s):  
Conrad Zeidler ◽  
Paul Zabel ◽  
Vincent Vrakking ◽  
Markus Dorn ◽  
Matthew Bamsey ◽  
...  

Author(s):  
Davit Hovhannisyan ◽  
Kareem Khalifeh ◽  
Peng Fei ◽  
Ahmed Eltawil ◽  
Fadi Kurdahi

1989 ◽  
pp. 154-158 ◽  
Author(s):  
J. E. Amadi-Echendu ◽  
E. H. Higham ◽  
P. J. Hurren

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