Using the internet of things for greenhouse temperature prediction, management, and statistical analysis

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dc.contributor.advisorBooysen, Thinusen_ZA
dc.contributor.authorHull, Keeganen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering.en_ZA
dc.date.accessioned2023-11-20T09:43:52Zen_ZA
dc.date.accessioned2024-01-08T19:16:33Zen_ZA
dc.date.available2023-11-20T09:43:52Zen_ZA
dc.date.available2024-01-08T19:16:33Zen_ZA
dc.date.issued2023-12en_ZA
dc.descriptionThesis (MEng)-- Stellenbosch University, 2023.en_ZA
dc.description.abstractENGLISH ABSTRACT: Unpredictable weather patterns caused by climate change are impacting agricultural productivity worldwide. This threatens sustainability and may lead to food insecurity, especially in developing regions. Affluent countries can afford costly investments toward mitigating the effects of climate change on food production. However, poorer countries tend to lag behind due to the lack of resources. To improve climate resilience, evolving technologies, such as the Internet of Things (IoT), have been proposed and developed for climate-smart farming. In this thesis, a technological solution was presented in the form of a digital twin for temperature monitoring and control of a greenhouse tunnel. Further, an aeroponics trial in the tunnel is statistically analysed for temperature variations due to the fan and wet wall temperature regulatory systems. A greenhouse tunnel in Stellenbosch, South Africa was instrumented with a prototype system to monitor temperatures inside the tunnel and control the cooling fan and wet wall. A generic hardware solution was then designed, assembled, and used in place of the prototype system to prove the feasibility of such a system in agriculture in South Africa. An analytical model was derived using the measurements as validation of the model. An empirical model using the Support Vector Regression algorithm was then developed and was used as comparison to the analytical model. The study was successful in producing an analytical model that was accurate to an acceptable degree. This physics-based model produced an RMSE of 2.93°C and an R2 value of 0.8. An empirical model was also produced that can simulate internal temperatures to an RMSE value of 1.76°C and an R2 value of 0.9 for a one-hour ahead simulation, outperforming the analytical model. The statistical analysis of the aeroponics system also showed a strong relationship between the temperature inside the system and the distance from the fan and wet wall. Finally, two analytical models of the irrigation process in a single container in the aeroponics system produced accurate results for the cooling effect of the irrigation (R2=0.901), while the unexpected heating effect when the fan was off produced less accurate results (R2=0.718). Future work stemming from this research includes improved data-driven modelling, cloud integration of the generic hardware, and fewer assumptions in analytical thermal modelling of the tunnel and container systems.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Onvoorspelbare weerspatrone veroorsaak deur klimaatverandering het ’n impak op landbouproduktiwiteit wˆereldwyd. Dit bedreig volhoubaarheid en kan lei tot voedselonsekerheid, veral in ontwikkelende streke. Welvarende lande kan duur beleggings bekostig om die effekte van klimaatverandering op voedselproduksie te verminder. Armer lande daarenteen bly agter weens ’n gebrek aan hulpbronne. Om klimaatweerbaarheid te verbeter, is ontwikkelende tegnologie¨e soos die Internet van Dinge (IoT) voorgestel en ontwikkel vir klimaatslim boerdery. In hierdie tesis is ’n tegnologiese oplossing aangebied in die vorm van ’n digitale tweeling vir temperatuurmonitoring en -beheer van ’n kweekhuis tonnel. Verder is ’n aeroponiese eksperiment in die tonnel statisties geanaliseer vir temperatuurvariasies as gevolg van die waaier en watermuur - beide deel van die temperatuurreguleringstelsel. ’n Kweekhuis tonnel in Stellenbosch, Suid-Afrika, is toegerus met ’n prototipe stelsel om temperature binne die tonnel te monitor en die koelwaaier en watermuur te beheer. ’n Analitiese model is afgelei deur die mates as bevestiging van die model te gebruik. ’n Empiriese model wat die “Support Vector Regression“ algoritme gebruik is toe ontwikkel en vergelyk met die analitiese model. Die studie was suksesvol in die produsering van ’n analitiese model wat akkuraat was tot ’n aanvaarbare mate. Hierdie fisika-gebaseerde model het ’n RMSE van 2.93°C en ’n R2-waarde van 0.8 gelewer. ’n Empiriese model is ook produseer wat interne temperature kan simuleer met ’n RMSE-waarde van 1.76°C en ’n R2-waarde van 0.9 vir ’n simulering een uur vooruit, wat beter presteer as die analitiese model. Die statistiese analise van die aeroponiese stelsel het ook ’n sterk verband tussen die temperatuur binne die stelsel en die afstand van die waaier en natmuur getoon. Ten slotte het twee analitiese modelle van die besproeiingsproses in ’n enkele houer in die aeroponiese stelsel akkurate resultate vir die koelingseffek van die besproeiing gelewer (R2=0.901), terwyl die onverwagte verhittingseffek wanneer die waaier af was, minder akkurate resultate gelewer het (R2=0.718). Toekomstige werk wat voortspruit uit hierdie navorsing sluit in verbeterde data-gedrewe modellering, wolk-integrasie van die generiese hardeware, en minder aannames in analitiese termiese modellering van die tonnel- en houerstelsels.af_ZA
dc.description.versionMastersen_ZA
dc.format.extentxv, 86 pages : illustrationsen_ZA
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/129014en_ZA
dc.language.isoen_ZAen_ZA
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subject.lcshInternet of things -- South Africaen_ZA
dc.subject.lcshGreenhouses -- Climate -- South Africaen_ZA
dc.subject.lcshSustainable agriculture -- South Africaen_ZA
dc.subject.lcshClimate change mitigation -- South Africaen_ZA
dc.subject.lcshPrecision farming -- South Africaen_ZA
dc.subject.lcshGreenhouse gas mitigation -- South Africaen_ZA
dc.titleUsing the internet of things for greenhouse temperature prediction, management, and statistical analysisen_ZA
dc.typeThesisen_ZA
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