A cost-effective canopy temperature measurement system for precision agriculture decision support – first-year status update

2015 ◽  
pp. 693-700 ◽  
Author(s):  
J. Martίnez ◽  
M. Pérez-Ruiz ◽  
G. Egea ◽  
L. Pérez ◽  
J. Agüera
Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3445 ◽  
Author(s):  
Christos Goumopoulos

This paper describes the design and calibration of a highly accurate temperature measurement system for pervasive computing applications. A negative temperature coefficient (NTC) thermistor with high resistance tolerance is interfaced through a conditioning circuit to a 12-bit digital converter of a wireless microcontroller. The system is calibrated to minimize the effect of component uncertainties and achieves an accuracy of ±0.03 °C on average (±0.05 °C in worst cases) in a 5 °C to 45 °C range. The calibration process is based on a continuous temperature sweep, while calibration data are simultaneously logged to reduce the delays and cost of conventional calibration approaches. An uncertainty analysis is performed to support the validity of the reported performance results. The described approach for interfacing the thermistor to the hardware platform can be straightforwardly adjusted for different thermistors, temperature ranges/accuracy levels/resolutions, and voltage ranges. The low power communication combined with the energy consumption optimization adopted enable an operation to be autonomic for several months to years depending on the application’s measurement frequency requirements. The system cost is approximately $45 USD in components, while its design and compact size allow its integration with extended monitoring systems in various pervasive computing environments. The system has been thoroughly tested and validated in a field trial concerning a precision agriculture application and is currently used in a health monitoring application.


2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110181
Author(s):  
Wei-Ling Lin ◽  
Chun-Hung Hsieh ◽  
Tung-Shou Chen ◽  
Jeanne Chen ◽  
Jian-Le Lee ◽  
...  

Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.


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