a fuzzy system

A Water-saving Irrigation System Based on Fuzzy Control Technology and Wireless Sensor Network

Peng Xiaohong College of Information Technology

Guangdong Ocean University Zhanjiang, P.R China

e-mail: [email protected]

Xiao Laisheng College of Information Technology

Guangdong Ocean University Zhanjiang, P.R China


Mo Zhi Technology Department

Zhanjiang New Zhongmei Chemical Industries Co.,Ltd. Zhanjiang, P.R China


Liu Guodong Maoming Branch

China Construction Bank Maoming, P.R China


AbstractA scheme is promoted which builds a water-saving irrigation system based on wireless sensor network and fuzzy control technology. we design a wireless sensor network, which consists of sensor node cluster, coordinator node and irrigation controller nodes Here sensor node cluster is responsible for gathering information such as soil moisture and regularly send it to the coordinator node. Fuzzy controller embedded in the coordinator node takes soil moisture error and error change rate as its input and obtained water demand amount of crops under certain soil moisture through fuzzy inference and fuzzy judge and output it to irrigation controller node. Irrigation controller node controls the implementation of automatic watering. In this article, we describe the implementation scheme of sensor network node and fuzzy controller in detail and design communication protocol for sensor nodes. The experimental results show that the system can quickly and accurately calculate water demand amount of crops, which can provide a scientific basis for water-saving irrigation. In this way, we have made an exploratory study in applying wireless sensor networks and fuzzy control technology to fine agriculture project.

Keywords-irrigation; fuzzy control technology; wireless sensor network

I. INTRODUCTION The implementation of precise control irrigation for crop

water demand information is one of the important ways to improve the utilization of water. There is a large amount of information of water demand of crops that should be collected and it is more convenient to realize long-distance data transmission by means of networks, Wireless sensor network technology has a broad application in many areas because of its advantages such as safe and reliable data transfer, simple and flexible network, low-cost equipment, long battery life, etc. [1,2] Therefore, nowadays wireless sensor networks are widely used in crop irrigation monitoring[3,4].

Because of the non-linear feature of soil moisture sensors, as well as its larger output delay, it is difficult to obtain satisfactory results to use traditional method of feedback control. Fuzzy control does not need to set up a precise mathematical model, only relies on people's experience and knowledge, and can imitate peoples thinking and experience to control multi-parameter complex system and make decision-making. Therefore, it shows a broad prospect in application of intelligent water-saving irrigation [5]. In 1996, Zhang etc. developed irrigation controller based on fuzzy control for greenhouse ornamental plants. This system uses a resistance-type soil moisture sensor as a detecting device and sensing devices is used to measure soil water leakage rate. This fuzzy controller was successfully running in the laboratory and greenhouse for a few months [6].

From a comprehensive analysis about water-saving irrigation research at home and abroad, we can see that, up to now, there is seldom control scheme of water-saving irrigation that use wireless sensor technology combined with fuzzy control and the research on it is not perfect enough in theory and application. Therefore, on the basis of research results at home and abroad, according to the specific characteristics of citrus seedlings watering in Guangdong province, combine wireless sensor networks with fuzzy control technology ,we have set up a low-cost, practical and intelligent water-saving irrigation system .we design a wireless sensor network, which consists of sensor node cluster, coordinator node and irrigation controller nodes[7]. Here, wireless sensor network is responsible for collection of information, such as positioning and soil moisture, and completes wireless real-time transmission of information of water demand of crops. Meanwhile, we use fuzzy control technology in system control scheme, we carry out intellectualized process to control watering time and watering amount.

978-1-4244-3693-4/09/$25.00 2009 IEEEAuthorized licensed use limited to: SASTRA. Downloaded on March 01,2010 at 05:43:24 EST from IEEE Xplore. Restrictions apply.

II. DESIGN OF SYSTEM STRUCTURE There are four parts in the system. They are sensor node

cluster, coordinator node and irrigation controller and irrigation pipe network. Wireless sensor network consists of sensor node cluster, coordinator node and controller node. Irrigation pipe network is laid over the irrigated areas and electric control valves are installed on pipelines. Deployment of humidity sensor node is to form a sensor node cluster according to the plant conditions and watering status of citrus seedlings. Each node is responsible for monitoring a small area of soil moisture conditions, real-time obtains soil moisture information, and sends the information to coordinator node with a certain time interval. When coordinator node receives the information of soil moisture sent by humidity sensor node, the fuzzy controller embedded in the coordinator node carries out fuzzy inference and fuzzy decision to soil moisture information in order to decide whether or not to conduct water and how long irrigation time is. Coordinator node then sends the irrigation information to irrigation controller node. Irrigation controller controls pump valve at corresponding region to open or turn off. Thus a closed-loop irrigation network control system is formed and it can implement water-saving irrigation to crops. Its principle of system structure is shown in Fig. 1.

III. IMPLEMENTATION OF SYSTEM HARDWARE Sensor node in wireless sensor network periodically

collects information of soil moisture and sends it together with the node location information to the coordinator node. Coordinator connects to irrigation controller node through the serial port and uploads data. Coordinator node and sensor node have the same hardware, only different on software configuration. The followings are the hardware designs for these two types of nodes.

A. Sensor Node and Coordinator Node Sensor node and coordinator node consist of data

acquisition module, data processing module, turn-off serial transceiver and power supply modules, as shown in Fig. 2. Data acquisition module uses digital humidity sensor SHT11, which is responsible for collecting the information of soil moisture on surveillance region. Data-processing module, we select Chipcon's SoC chip CC2431 It is responsible for controlling the operation of the entire sensor node, including storage and processing data collected by itself, implementation of wireless data transceiver. Single-chip microcomputer in CC2431 communicates with sensor by its figure port. Self-shutdown serial transceiver SP3223 is a transceiver that can automatically work or shut down according to the state of serial cable connection. When there is no serial cable connection or without data communications, the chip is in shutdown state and its consumption of current is only 1A. Coordinator node connects to the irrigation controller through RS232 serial interface in chip SP3223. Energy supply modules, CR2032 is selected, is responsible for energy supply of entire node.

B. Irrigation Controller Node Irrigation controller takes low-power ARM processor

S3C44B0X that is based on the ARM7TDMI-S core as its main part, which is a cost-effective microcontroller solution scheme

designed for Samsung's handheld devices and general applications. In the system, a lot of modules are used, such as an 8-way 10-bit A/D converter module in the S3C44B0X, LCD controller, 32-bit timers, UART, GPIO, PWM output module, etc. Irrigation controller receives the information transmitted from the serial port of coordinator and sends corresponding irrigation command through I/O port to control the action of electric control valve. Optocoupler isolation is set between general-purpose I/O ports in microprocessor and control valves, each I/O port corresponding to an electric control valve. S3C44B0X has as many as 71 multi-function I/O ports, so the system has a high expansibility. The composition of irrigation controller node is shown in Fig. 3.

C. Communication Protocol When designing a wireless sensor network, the first thing

needed to consider is how to get a low power consumption and how to prolong the network life cycle as long as possible. In this paper, IEEE802.15.4 standard and ZigBee wireless network technology are introduced to design the wireless sensor network. IEEE802.15.4 standard defines the physical layer and MAC layer. ZigBee defines the network layer protocol [8]. ZigBee is a close range, low-complexity, low power, low data rate, low-cost two-way wireless communications technology that is suitable for the system [9]. Because the primary energy consumption is at idle listening, receiving unnecessary data and retransmission of collisions and so on, combining the characteristics of self-localization and RTC wake-up call in CC2431, in order to reduce power consumption the network uses non-beacon access mode, namely ALOHA CSMA/CA channel access mechanism. Sensor node dialogues with coordination node only when required for data transmission and it is in sleep the rest of the time in order to save the energy consumption to maintain the network connection. Since each sensor node needs to transmit rare amount of information and the time of dormant state is larger than its working hours, collision of information is almost unlikely.

Figure 1. Principle of system structure

Figure 2. Structure of sensor node

Authorized licensed use limited to: SASTRA. Downloaded on March 01,2010 at 05:43:24 EST from IEEE Xplore. Restrictions apply.

Figure 3. Composition of irrigation controller node

IV. DESIGN OF IRRIGATION FUZZY CONTROLLER For the fuzzy controller embedded in coordinator node, its

input variables are E and E, its output variable is T, where E is the error between the actual soil water amount and the soil water amount which is most suitable for citrus seedlings normally to grow, E is error change rate calculated from current error and the previous one, T is the length of time for irrigation. According water demand of crops at different growth stages, combined with agricultural experts outcome of long-term research and practical experience of the operators, fuzzy control rule table can be summed up. Corresponding control decision-making can be derived from control rules. Then the corresponding control amount can be obtained due to the principle of maximum membership degree. Through offline and repeated calculation and debugging by computer, finally, we can get fuzzy control query table for practical application and store it in the coordinator node. When the system runs, the first step is to calculate E and E according to the input data, and quantify them to calculate the universes of discourse of fuzzy amount. Then by searching fuzzy control table, we can achieve the current water demand information of crops at any area of irrigation.

A. Fuzzy Process of Input and Output Variables In this paper, we set soil moisture error E between [-0.8, 0],

error change rate E between [-0.6, 0.6], irrigation time T between [0, 15 min].

The universes of discourse of input variable E are {-3, -2, -1, 0, 1, 2, 3}. Its fuzzy language values are {NL (very dry), NM (dry), NS (relatively dry), ZO (moderate), PS (relatively wet), PM (wet) and PB (very wet)}. The universes of discourse of E are {-3, -2, -1, 0, 1, 2, 3}. Its fuzzy language values are {NL (serious loss of water), NM (relatively serious loss of water), NS (no serious loss of water), ZO (no change of water), PS (little increase of water), PM (relatively more increase of water) and PB(more increase of water)}. Membership function of E and E is shown in Fig. 4.

The universes of discourse of T are {0, 5, 10, 15}. Its fuzzy language values are {ZO (no watering), PS (watering for a short time), PM (watering for a relatively long time) and PL (watering for a long time}. Its membership function is shown in Fig. 5.

B. Fuzzy Rule Table In general, control rules of fuzzy controller can be summed

up from experts professional knowledge and operators experience. According to water requirement of crops at different growth stages, combined with agricultural experts outcome of long-term research and on-site operator's practical experience, we set up fuzzy control rule table which is shown in TABLE I.

C. Foundation of Fuzzy Control Query Table Two-input & single-output language control strategy shown

in TABLE 1 is composed of 49 fuzzy conditional statements. We can obtain the precise amount of T through removing its fuzzy value and according to the largest membership law and calculation of its fuzzy relation matrix[10]. So we can get fuzzy control query table which is shown in TABLE II.

Figure 4. Membership degree of input var. E & E

0 5 1 0 1 5

Z O P S P M P L

Figure 5. Membership degree of output var. T

TABLE I. FUZZY CONTROL RULE TABLE

NL NM N S ZO PS PLPM

E

N L

N M

N S

ZO

PS

PM

PL ZO ZO ZO ZO ZO ZO ZO

PS ZO ZO ZO ZO ZO ZO

PM PS

PL PM

PL PL

PL PL

PL PL PL PM PS PS ZO

PM PM PS ZO ZO

PM PS ZO ZO ZO

ZO ZO

ZO ZO ZO ZO

ZOPS ZO

ZO

E

TABLE II. FUZZY CONTROL QUERY TABLE

-3 -2 -1 0 1 32

E

-3

-2

-1

0

1

2

3 0 0 0 0 0 0 0

5 0 0 0 0 0 0

10 5

15 10

15 15

15 15

15 15 15 10 5 5 0

10 10 5 0 0

10 5 0 0 0

0 0

0 0 0 0

05 0

0

E


V. DESIGN OF SYSTEM SOFTWARE Sensor node separately collects soil temperature at each

sampling cycle and transmits data to the coordinator node. The fuzzy controller embedded in the coordinator node carries out fuzzy reasoning and fuzzy decision according to soil moisture and the actual water demand of crops in order to decide whether or not to conduct water and how long irrigation time is. Coordinator node then sends the irrigation information to irrigation controller node. Irrigation controller controls pump valve at corresponding region to open or turn off. Thus a closed-loop irrigation network control system is formed and it can implement water-saving irrigation to crops. After a sampling period, each sensor node immediately comes into state of hibernation to conserve energy until the next cycle. Flow chart of software design for sensor node, coordinator node and irrigation controller node are expressed separately in Fig. 6, Fig. 7 and Fig. 8.

Figure 6. Software flow chart of sensor node

Figure 7. Software flow chart of coordinator node

Figure 8. Software flow chart of irrigation controller node

VI. CONCLUSIONS In this paper, wireless sensor networks and fuzzy control

technology are introduced to design a water-saving irrigation system. By means of the characteristics that wireless transmission is conducive to fruit seedlings irrigation, we design a wireless sensor network and solve the problem of real-time transmission of irrigation information. We apply fuzzy control technology to intelligent water-saving irrigation, which can meet the requirement and development of intellectualized and network-based agriculture. We believe that the exploratory study applying wireless sensor networks combined with fuzzy control technology to precise agriculture projects must have a broad popularization value and application prospect in the future.

REFERENCES [1] Sun Limin, Li Jianzhong, Chen Yu, Wireless sensor networks,

Beijing: Tsinghua university press, 2005, pp.1-20. [2] Cui li Jue Hailing etc., Overview of Wireless Sensor Networks,

Journal of Computer Research and Development, 2005,42(1), pp.163-174.

[3] Fang Xujie, Zhou Yiming, Chen Wenliang, Ding Chaohong,Yang Xianglong, The design of wireless intelligent irrigation systan based on ZigBee technology, Research on Mechanization of Farming, 2009.1, pp.114-118.

[4] Zeng Liancheng, Luo Zhixiang, Xie Zhijian, Auto system based on WSN for water-saving irrigation of farmland, Farming Network Information, 2008.11, pp.13-15.

[5] Guo Zhengqin, Wang Yiming, Yang Weizhong, Feng Lei, Yang Shaohui, Intelligent irrigation control system based on fuzzy control, Research on Mechanization of Farming, 2006.16, pp.103-108.

[6] Zhang Q, John, Ken T, Application of fuzzy logic in an irrigation control system, Proceedings of the IEEE international conference on industrial technology[C], 1996.

[7] Luo Huiqian, Zhang Qing, Qiao Xiaojun, Research and design of wireless sensor networks hardware flat roof in agriculture, Microcomputer Applications, 2006, 27(5), pp.534-537.

[8] IEEE 802.15.4 web site, http://www.ieee802.org/15/pub/TG4.html. [9] Patrick Kinney, etc, ZigBee technology: Wireless control that simply

works, Communications design conference, Oct 2, 2003. [10] Junpu Wang, Intelligent Control, Press of University of Science and

Technology of China, 1996, pp.90-135.


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a fuzzy system

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