Based on wireless sensor network (WSN) technology and crop growth simulation techniques, this paper shows the design and realization of an automatic
monitoring and closed-loop control system in greenhouses. Firstly, a multi-hop network communication method based on clustering and simple
medium access control that is suitable for the monitoring of a large-scale greenhouse environment is designed and analysed, and the simulation results
show that its lifetime is 10% longer than LEACH (low energy adaptive clustering hierarchy) when 1% and 20% nodes die. Secondly, a physiological development
day-based crop growth simulation model will be built to predict the tomato growth and make further decisions in adjusting the greenhouse climate.
In order to obtain the model indicators, early experiments were carried out on four kinds of tomato variety, and the experiment results show
that the proposed model has a higher accuracy than the effective temperature model on the root mean square error within 1–4 days, and on the mean
absolute error within 2–4 days. Finally, according to the proposed methods, a comprehensive greenhouse dynamic monitoring and closed-loop control
system with a 60 MC13213 nodes WSN was implemented. The implementation results show that with three AAA Ni–MH (nominal capacity 750 mAh)
batteries, 80% nodes maintained a survival time of 45–60 days, and the model prediction compared with the observed value is at a high correlation
efficient of 95%.
Based on wireless sensor network (WSN) technology and crop growth simulation techniques, this paper shows the design and realization of an automaticmonitoring and closed-loop control system in greenhouses. Firstly, a multi-hop network communication method based on clustering and simplemedium access control that is suitable for the monitoring of a large-scale greenhouse environment is designed and analysed, and the simulation resultsshow that its lifetime is 10% longer than LEACH (low energy adaptive clustering hierarchy) when 1% and 20% nodes die. Secondly, a physiological developmentday-based crop growth simulation model will be built to predict the tomato growth and make further decisions in adjusting the greenhouse climate.In order to obtain the model indicators, early experiments were carried out on four kinds of tomato variety, and the experiment results showthat the proposed model has a higher accuracy than the effective temperature model on the root mean square error within 1–4 days, and on the meanabsolute error within 2–4 days. Finally, according to the proposed methods, a comprehensive greenhouse dynamic monitoring and closed-loop controlsystem with a 60 MC13213 nodes WSN was implemented. The implementation results show that with three AAA Ni–MH (nominal capacity 750 mAh)batteries, 80% nodes maintained a survival time of 45–60 days, and the model prediction compared with the observed value is at a high correlationefficient of 95%.
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