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Title: Automated Climate Monitoring System: the Case of Greenhouse Industries in Ethiopia
Authors: Weldeslasie, DT
Assres, G
Grønli, TM
Ghinea, G
Keywords: Automation;Greenhouse;Climate Control;Sensors;Temperature;Light;ACMS
Issue Date: 3-Jul-2021
Publisher: Elsevier BV
Citation: Weldeslasie, D.T. et al. (2021) ‘Automated Climate Monitoring System: the Case of Greenhouse Industries in Ethiopia’, Internet of Things. Vol. 15, pp.1-10 doi:10.1016/j.iot.2021.100426.
Abstract: Technological interventions can play a significant role to keep optimal growing conditions in greenhouse industries with varying seasons and improve environmental performance by maintaining heating, cooling, and humidity levels. The research reported in this paper aims to integrate an Automated Climate Monitoring System (ACMS) for greenhouse in Ethiopia. Green- houses in Ethiopia require constant monitoring to properly maintain optimal climate conditions to grow plants all year round. To this end, they apply environment monitoring systems like timing devices, ON/OFF controllers and manual (human) monitoring which are prone to error. We propose a flexible ACMS prototype which can be applied without a user having the knowledge of how the system is made. The prototype system uses Advantech PC- cards and maintains the optimal climate condition (temperature, humidity and light intensity) of the greenhouses. Moreover, the easy to use graphical user interface provided by the system enables to combine the hardware and software functionalities for optimal manipulation of the environmental conditions. The system is centralized in that anyone can control several de- vices spread in the environment being monitored from one Computer. The performance of the system is found to be 88.89% which is “accurate and acceptable” result according to our confusion matrix. This indicates that better environmental supervision can be achieved when using the system. Moreover, the mean user acceptance (by the flower growers) of the ACMS and timing devices, ON/OFF implementations is 84.6% and 73.2%, respectively, highlighting the potential for integrating the ACMS into the greenhouse industry.
ISSN: 2542-6605
Appears in Collections:Dept of Computer Science Research Papers

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