An Intelligent Irrigation System Design For Tomatoes Based On The Internet Of Things

The little possession rate on average, imbalance distribution in space, hardly use, and the pollution result in the water resource urgency situation in China, which has been seriously restricting the development of economy. Since the agricultural use occupies the largest proportion in the use of water resource, advanced water saving irrigation system design can improve the economic benefit and the ecological environment and achieve the goal of saving water; this article presents a conception of water-saving ways from the perspective of agricultural irrigation.In this paper, a set of intelligent irrigation system is designed based on the technology of the Internet of things(IOT). In the first place, system uses the ZigBee terminal nodes to collect the environmental information, which is required to be monitored, and then sends it to GPRS through the coordinators. The network layer is responsible for the information’s delivery to the Internet through the GPRS, and eventually to the server in the application layer. In the end, we adopt the processing algorithm, which is designed on the server, to deal with the monitoring data, give the irrigation instructions according to the irrigation strategies and use the executive program of terminal nodes to perform. In the actual development of the whole process, the author has made a deep study on each link of the main idea and has achieved the following results:1. Article studies the influence factors of crops’ water requirement from the aspects such as farmland hydrological cycle, crop physiology cycle and so on. In this study, a hybrid model that integrated Nonlinear Principal Component Analysis (NLPCA) method with the radial basis function (RBF) neural network (NLPCA-RBF) was used for estimating Tomato ET. On the premise that not only the integrity of the meteorological information can be guaranteed but the correlation among different factors can be eliminated, NLPCA was applied to simplify the seven main meteorological factors which related to the ET into three principal components.Then used these components as input and the measured ET as output target, therefore, the network was created and verified by parts of the data were not used in design of the network. By comparing with RBF neural network, the results showed that NLPCA-RBF network model can well reflected the relationship between meteorological factors and evapotranspiration and got more accurate result.2. Considering that irrigation system generally has the defects of hysteresis and nonlinearity, an irrigation system, which regards the fuzzy controller as the irrigation strategy, is introduced in this paper. Compared to a single input factor, the fuzzy controller input choose two factors:soil moisture and evaporation, which will make the irrigation strategy more accurate.3. This system adopts modular design thought. It uses MC9S12XS128 micro controller as the core, and joins the corresponding functional modules to realize the design of ZigBee node information acquisition module, communication module and the actuator, which makes the ZigBee network be able to complete information collection, transmission, and the implementation of irrigation. The designed irrigation system server uses MVC framework, capable of mandatory application data display, data processing and process control, which can improve the development efficiency and make it easy to modify.