Greenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligence

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dc.contributor.authorShamshiri, Redmond R.en
dc.contributor.authorHameed, Ibrahimen
dc.contributor.authorThorp, Kellyen
dc.contributor.authorBalasundram, Sivaen
dc.contributor.authorShafian, Sanazen
dc.contributor.authorFatemieh, Mohammaden
dc.contributor.authorSultan, Muhammaden
dc.contributor.authorMahns, Benjaminen
dc.contributor.authorSamiei, Sabaen
dc.contributor.editorShamshiri, Redmond R.en
dc.date.accessioned2022-01-13T19:03:51Zen
dc.date.available2022-01-13T19:03:51Zen
dc.date.issued2021-06-16en
dc.date.updated2022-01-13T19:03:49Zen
dc.description.abstractAutomation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dual-core 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments.en
dc.description.notesYes (Peer reviewed?)en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier1 (Chapter number)en
dc.identifier.doihttps://doi.org/10.5772/intechopen.92515en
dc.identifier.urihttp://hdl.handle.net/10919/107617en
dc.language.isoenen
dc.publisherIntechOpenen
dc.relation.ispartofNext-Generation Greenhouses for Food Securityen
dc.relation.urihttps://www.intechopen.com/chapters/76695en
dc.relation.urihttps://www.intechopen.com/en
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectLoRaWANen
dc.subjectGreenhouseen
dc.subjectDataloggeren
dc.subjectIOTen
dc.subjectAgroTechen
dc.subjectLeaf wetnessen
dc.titleGreenhouse Automation Using Wireless Sensors and IoT Instruments Integrated with Artificial Intelligenceen
dc.typeBook chapteren
dc.type.dcmitypeTexten
dc.type.otherChapteren
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/CALS T&R Facultyen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/School of Plant and Environmental Sciencesen

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