Development of Internet of Things based smart multi-sensors system for early prediction of plant growth

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dc.contributor.authorGhavami, Maryam
dc.contributor.examiningcommitteePaliwal, Jitendra (Biosystems Engineering)en_US
dc.contributor.examiningcommitteeHervet, Vincent (Entomology)en_US
dc.contributor.supervisorErkinbaev, Chyngyz
dc.contributor.supervisorJian, Fuji
dc.date.accessioned2022-05-30T20:02:00Z
dc.date.available2022-05-30T20:02:00Z
dc.date.copyright2022-05-27
dc.date.issued2022-05-27
dc.date.submitted2022-05-27T21:59:26Zen_US
dc.degree.disciplineBiosystems Engineeringen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractThe application of Internet of Things (IoT) has become an important part of our daily lives in diverse areas. IoT provides the ability to integrate and communicate between different objects using smart sensors, cameras, and actuators through an Internet connection. In recent years, a combination of IoT technologies have begun to play an important role in monitoring plant health and growth condition in agricultural systems. Monitoring plant conditions and the effect of abiotic stresses in the early stages is very crucial since it can maximize crop productivity and enable producers to provide products of superior quality. The objective of this research study was to design, develop, and deploy a Raspberry Pi-based smart multi-sensor system for real-time monitoring of plant health conditions at various soil moisture levels. The developed prototype was successfully tested by conducting a series of calibration tests at known soil moisture and temperature conditions. The results obtained from five calibration tests demonstrated that the temperature and soil moisture sensors were accurate and robust over the selected period. The Raspberry Pi-based smart imaging enabled capturing images of plants in real-time for predicting their health and growth condition. To predict the critical time for irrigation, mathematical models were developed that established a relationship between the number of green (i.e., healthy) areas of the plant and soil moisture condition for each soil moisture content (i.e., 0, 20, 40, 60, and 80%). It was observed that the value of the green area of plants decreased with a decrease in soil moisture content. These models could be applied for integrating IoT-based systems in various environmental conditions.en_US
dc.description.noteOctober 2022en_US
dc.identifier.urihttp://hdl.handle.net/1993/36507
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectInternet of Thingsen_US
dc.subjectsmart sensorsen_US
dc.subjectRaspberry Pi-based smart multi-sensor systemen_US
dc.subjectgreen area of planten_US
dc.subjectMonitoring plant conditionsen_US
dc.titleDevelopment of Internet of Things based smart multi-sensors system for early prediction of plant growthen_US
dc.typemaster thesisen_US
project.funder.identifierhttp://dx.doi.org/10.13039/501100003288en_US
project.funder.nameBell Canada Enterprisesen_US
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