Building a Diagnostic Model for Climate Controlled Greenhouse using Bayesian Network
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Abstract
Greenhouse crop production is directly influenced by climate conditions. The aim of this study is to achieve adequate inside climate conditions (mainly air temperature, photosynthetically active radiation, CO2 concentration, and humidity) of controlled greenhouses located in Pathum Thani province of Thailand. The adequacy of Bayesian diagnosis to model the environmental conditions of a greenhouse as essential parameters including disturbances such as external temperature, external radiation, wind speed, wind direction, external humidity, external CO2, and soil temperature. The system is built and tested using data gathered from a real greenhouse located in Pathum Thani province under closed-loop control. The Bayesian network has demonstrated to provide a good approximation of a control signal and the results show the performance and applicability of Bayesian networks within the proposed climate controlled greenhouse solution.
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References
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