A novel and smart automatic light-seeking flowerpot for monitoring flower growth environment

Although the flowerpot is widely used for indoor flowers,it cannot meet the needs of intelligent management during the uncared-for period.The objective of this study was to design a new microcontroller-based smart flowerpot.Its overall system was composed of three parts:information collection layer,automatic control layer and data transmission layer.Firstly,in the process of collecting information,the Laiyite criterion and the normalized weighted average algorithm were adopted to improve the accuracy of information collection.Secondly,for making precise control decisions,the fuzzy control was used to achieve automatic on-demand watering.Finally,the method for comparative analysis of regional light intensity was utilized to achieve light-seeking and light-supplementing.Experimental results showed that the smart flowerpot had strong anti-jamming performance for information collection,the relative soil moisture of flowers could be stably maintained near the optimum humidity(65%),and the light was well-distributed on the flower with the error angle of light-supplementing ranged from-3°to 3°.

A quasi-steady state model for predicting the heating requirements of conventional greenhouses in cold regions

A time-dependent,quasi-steady state thermal model(GREENHEAT)based on the lumped estimation of heat transfer parameters of greenhouses has been developed to predict the hourly heating requirements of conventional greenhouses.The model was designed to predict the hourly heating requirements based on the input of greenhouse indoor environmental control parameters,physical and thermal properties of crops and construction materials,and hourly weather data including temperature,relative humidity,wind speed,and cloud cover.The model includes all of the heat transfer parameters in greenhouses including the heat loss for plant evapotranspiration,and the heat gain from environmental control systems.Results show that the predicted solar radiation data from the solar radiation sub-model are a reasonable fit with the data from the National Solar Radiation Database(NSRDB).Thermal analysis indicates environmental control systems could reduce 13–56%of the total heating requirements over the course of a year in the study greenhouse.During the winter season,the highest amount of greenhouse heat is lost due to conduction and convection,and the heat used for evapotranspiration is dominant in the summer.Finally,the model was validated with actual heating data collected from a commercial greenhouse located in Saskatoon,and the results show that the model satisfactorily predicts the greenhouse heating requirements.