Study On Intelligent Management System For Greenhouse Climate Based On Internet Of Things

The technology for the Internet of Things (IOT) is to connect everything to the Internet for information exchange and telecommunications through a variety of information sensing devices according to the given protocols, so as to realize the intelligentized identification, location, tracking, monitoring and management. IOT technology has been applied to various fields in recent years. In this paper, after analyzing the research status of the technology of Internet of things for intelligent measurement and controlling technology in the greenhouse environment, the studies have made for the wireless measurement and control system of greenhouse climate based on multi-sensor information fusion, climate information processing, crop model base, and the optimization of scale environmental regulation. The details of the sudies for the greenhouse intelligent management system based on Internet of things main include as followings:(1) The measurement and controlling system of wireless sensing networks for greenhouse climate based on Zigbee/3G has been built, and the multi-sensor information fusion models of node/cluster layers were proposed according to the characteristics of the three-layer structure of wireless sensing network. The node level fusion has been conducted by using the kalman filtering model, and the cluster level fusion by using weighted least square model. The experimental results showed that the two-level fusion models can improve the measurement accuracy and stability of the wireless sensing networks.(2) According the climate information measured with WSN system of the greenhouse environment and in conformity to climate control rules, the controlling-effect model of greenhouse climate has been proposed on the base of support vector machine (SVM) and multi-model switching models. The outdoor meteorological forecast models have been established based on least squares support vector machine (LSSVM) algorithms with the online learning. By using the incremental learning LSSVM algorithms, the greenhouse climate effect forecast models have been establish, and the multi-model switch controllers have been used to realize the adaptive switch of control effect models. The established climate control effect models were verified through the field experiments, and the results showed that the climate control effect models can obtain the satisfactory prediction precision, thus realizing the adaptive switch of climate control effect models. The management system of wireless sensor network resource based on multi-Agent has been established to realize the management of wireless sensors.(3) In order to accomplish the production planning of the vegetables in the greenhouse targeting for the sale of certain date, the environmental plan decision support models for long-scale greenhouse have been established on the basis of the temperature-accumulated models of crops and by using the history climate data and market price information etc. This model can achieve the decision support for the forecast of sale date and price with the given plant establishment, and determine the day-by-day optimized decision of environment, as well as to make the day-by-day optimized decision of the environment in the conditions of predicted plant establishment date and the certain sale date, while making the daily optimized decision of the environment of the greenhouse operation etc.(4) In order to realize the coordination between the long-scale changes of crop growth and short-scale of climate changes, the realtime greenhouse climate control parameters optimal model was proposed on the basis the multi-model fusion. The model was integrated for the fusion of the control model of accumulation temperature model, settings of model and the light-based models, where the mutil-model fusion is realized by using the D-S evidence theory. Meanwhile, the experimental model with the multi-model fusion was verified in the testing greenhouse. The results show that the multi-model fusion controlling models can increase the accumulation temperatures more than those of the setting values for model, thus realizing the yields of greenhouse.(5) In order to resolve the issues of poor generality and repeatability of the crop simulation models, the systematic framework has been established for the greenhouse crop growth simulation model database on the basis of multi-agent. The model database consists of the three layers of access layer, model integration layer and data layer. And it is designed as the model agent, management agent, catalog server agent, communication system and access agent. The Agent-based software of the crop growth simulation model in the greenhouse was developed on the JADE platform. And the growth simulation model for the cucumber was used to test this system.(6) The standardized information exchange interface has been established for crop growth environment information in the greenhouse on XML. The Agent-based greenhouse climate controlling decision support system was developed. The software of greenhouse intelligent management system for IOT was developed, so the system can be integrated with WSN resources management, the greenhouse climate controlling decision support system and information exchange interface. This system has succeeded to be applied in the greenhouse of Ruijin Agricultural Scientific and Technologic demonstration park at Jinkou district of Zhenjiang, which can have realized the intelligentized management for greenhouse.