Study on the Intelligent Control Model of a Greenhouse Flower Growing Environment

Intelligent control of the greenhouse planting environment plays an important role in improving planting efficiency and guaranteeing the quality of precious flowers.Among them,how to adapt the air humidity,temperature and light intensity in greenhouses to the different needs of the flower growth cycle is the key problem of intelligent control.Therefore,an intelligent flower planting environment monitoring and control system model(named)based on the Internet of Things and fuzzy-GRU network adaptive learning is proposed.The above three parameters in the greenhouse were used as model input parameters.The optimal growth humidity,temperature and illumination intensity of flowers are determined by the model,and the output temperature,humidity and illumination intensity act on the executing organ of the greenhouse room by the single-chip microcomputer.The model was evaluated using field greenhouse crops.The results show that the performance of this model is better than that of the PID model and fuzzy control model in simulation experiments and actual scene control.Compared with the flowers in the natural state,the plants of the flowers under systematic control were approximately 6 cm higher than those in the natural state on average,the blooming time of the flowers was approximately two days longer than that in the natural state,and the quality of the flowers was stable.