Wireless Monitoring Of The Green House Parameters Computer Science Essay

Published: Last Edited:

This essay has been submitted by a student. This is not an example of the work written by our professional essay writers.

Plant growth is affected by various factors. The most important factors for the quality and productivity of plant growth are temperature, humidity, light. Continuous monitoring of these environmental variables gives information to the grower to better understand, how each factor affects growth and how to manage maximal crop productiveness. The optimal greenhouse climate adjustment can enable us to improve productivity and to achieve remarkable energy savings - especially during the winter in northern countries. The main purpose of a greenhouse is to provide and maintain the environment that will result in optimum crop production or maximum profit. This includes an environment for work efficiency as well as for crop growth. There has been much research and design about environment control using sophisticated technology (automated or computerized).The new tools and technologies applicable to take the advantage of advances in electronics and computers such as RS, GPS, GIS and WSN. These technologies cover three aspects such as data collection, analysis or processing of recorded information and recommendations based on available information.

In the present paper, authors have reviewed the new technologies and have given an emphasis on comparatively cheaper system suitable for developing countries such as India, Pakistan, Bhutan, Bangladesh and Srilanka. The farmers in these countries can easily use the system under design by the authors for increasing the yield due to in time correction of the important parameters such as temperature and humidity.

Keywords: Environmental Parameters, RS, GPS, GIS, WSN, AVR, RF2.4, Microcontrollers.

General terms: Green house, Microcontrollers, AVR, RF2.4.


A. MAPPING: The generation of maps for crop and soil properties is the most important and first step in advanced farming techniques. These maps will measure spatial variability and provide the basis for controlling spatial variability. Data collection occurs both before and during crop production and is enhanced by collecting precise location coordinates using the GPS. Fig. 1shows the mapping system installed in food harvester.

Figure - 1: Mapping system installed in food harvester

The data collection technologies are grid soil sampling, yield monitoring, RS and crop scouting. During crop production, the data are collected through sensing instruments such as soil probes, electrical conductivity and soil nutrient status. Optical scanners are used to detect soil organic matter and to recognize weeds. Then these data generated through mapping are recorded and stored in a computer system for future action and generated maps used for acquisition of information and for making strategic decisions to control variability. Mapping can be done by RS, GIS and manually during field operations.


It is the acquisition of information about an object from a distance, with precision, without coming into contact with the same. Although the use of RS is a decade old, its relevance to agriculture in spatial variability management is relatively new. RS measures visible and invisible properties of a field or a group of fields and converts point measurements into spatial information, to monitor temporally dynamic plant and soil conditions. The visual observations are recorded through a digital notepad and geo-referenced to GIS database, the most commonly used RS device, but aerial photography and videography are also used in PF5. Satellite RS has provided a tool for acreage estimation one month in advance, with more than 95% accuracy and in mono-crop area yield estimation with more than 90% accuracy ten days in advance. Finally, images are used for generating maps and calibration of the measurement, assuming that measurements are taken in field to ground-truth accuracy. These images allow mapping of crop, pest and soil properties for monitoring seasonally variable crop production, stress, weed infestation and extent within a field. RS can be used for PF in a number of ways for providing input supplies and variability management through decision support system. The point data of soil test results can be translated into spatial coverage based on geostatistical interpolation, which gives chemical properties of the soil, nutrient status, organic matter, salinity, moisture content, etc. This information on spatial variability can be used with other geo-references to identify both seasonally stable and variable units, based on which management strategies can be developed. Fig.2 shows the of schematic of remote sensing using planes.

Space technology combined with satellite RS and communication provides valuable, accurate and timely information like early warning, occurrence, progressive dangers, damage assessment, quick dissemination of information regarding disaster and decision support to mitigate it.

Figure - 2: Schematic Remote Sensing Using Planes


Recently, use of GIS in agriculture has increased because of misuse of resources like land, water, etc. GIS is the principal technology used to integrate spatial data coming from various sources in a computer. GIS techniques deal with the management of spatial information of soil properties, cropping systems, pest infestations and weather conditions. This is primarily an intermediate step because it combines the data collected at different times based on sampling regimes, to develop the subsequent decision technologies such as process models, expert systems, etc. The manipulation of spatial information had begun in the 1960s, but has grown rapidly with the development of computer-aided techniques. In the new millennium, GIS-aided techniques are indeed needed for sustainable food production and resource utilization, without further depletion of the environment. Fig.3 shows a fully operational GIS System with other controls.

Figure - 3: A fully operational GIS System with

other controls

GIS technology will help the farmers and scientists in decision making, as precise information on field will be readily available. GIS techniques make weed control, pest control and fertilizer application site-specific, precise and effective; it would also be very useful for drought monitoring, yield estimation, pest infestation monitoring and forecasting. GIS coupled with GPS, microcomputers, RS and sensors is used for soil mapping, crop stress, yield mapping, estimation of soil organic matter and available nutrients. In combination these technologies have brought out rapid changes in data collection, storing, processing, analysis and developing models for input parameters.


A recent survey of the advances in wireless sensor network applications has reviewed a wide range of applications for these networks and identified agriculture as a potential area of deployment together with a review of the factors influencing the design of sensor networks for this application. WSN is a collection of sensor and actuators nodes linked by a wireless medium to perform distributed sensing and acting tasks. The sensor nodes collect data and communicate over a network environment to a computer system, which is called, a base station. Based on the information collected, the base station takes decisions and then the actuator nodes perform appropriate actions upon the environment. This process allows users to sense and control the environment from anywhere. There are many situations in which the application of the WSN is preferred, for instance, environment monitoring, product quality monitoring, and others where supervision of big areas is necessary. Fig.4 shows the schematic operation of wireless sensor network.

Figure - 4: Schematic Operation of Wireless Sensor Network

When a large number tiny sensor nodes are deployed either randomly or in regular grid, they shall act collectively to perform sensing over a large area or in inaccessible terrains.

Wireless sensor network (WSN) thus form a useful part of the automation system architecture in modern greenhouses. Wireless communication can be used to collect the measurements and to communicate between the centralized control and the actuators located to the different parts of the greenhouse. In advanced WSN solutions, some parts of the control system itself can also be implemented in a distributed manner to the network such that local control loops can be formed. Compared to the cabled systems, the installation of WSN is fast, cheap and easy. Moreover, it is easy to relocate the measurement points when needed by just moving sensor nodes from one location to another within a communication range of the coordinator device. If the greenhouse flora is high and dense, the small and light weight nodes can even be hanged up to the plants' branches. WSN maintenance is also relatively cheap and easy. The only additional costs occur when the sensor nodes run out of batteries and the batteries need to be charged or replaced, but the lifespan of the battery can be several years if an efficient power saving algorithm is applied.

The research on the use of WSN in agriculture is mainly focused on two major areas:

(I) Experimental or simulation work on various routing protocols and network topologies to increase data transfer rates whilst maintaining or reducing power consumption.

(II) Proof-of-concept applications to demonstrate the efficiency and efficacy of using sensor networks to monitor and control agriculture management strategies.


On the other hand, event-based systems are becoming increasingly commonplace, particularly for distributed real-time sensing and control. A characteristic application running on an event-based operating system is that where state variables will typically be updated asynchronously in time, for instance, when an event of interest is detected or because of delay in computation and/or communication. Event-based control systems are currently being presented as solutions to many control problems. In event-based control systems, the proper dynamic evolution of the system variables is what decides when the next control action will be executed, whereas in a time-based control system, the autonomous progression of the time is what triggers the execution of control actions. The fundamental reason for the predominance of the time-based control systems has been based on the existence of a well established theory for control systems with a constant sampling time. However, current distributed control systems also impose restrictions on the architecture of the system that makes difficult the adoption of a paradigm based on events activated per time. For instance, in the case of closed-loop control using computer networks or buses (such as field bus, local network area, or Internet), where asynchronous communication is required. An alternative to these approaches consists of using event-based controllers that are not restricted to the synchronous occurrence of controller actions. The employment of synchronous sampling period is one of the severest conditions that control engineers follow for implementation tasks. Many examples can be found, such as mobile phones, printing devices, or PDA's. The complexity of these devices (processes), as well as the complexity of the controller, is increasing very fast. These requirements can be reduced with event-based controllers, where the control actions can be executed in an asynchronous way.

Control problems in greenhouses are mainly focused on fertirrigation and climate systems. The fertirrigation control problem is usually solved providing the amount of water and fertilizers required by the crop. The climate control problem consists in keeping the greenhouse temperature and humidity in specific ranges despite of disturbances. Adaptive and feed forward controllers are commonly used for the climate control problem. Therefore, fertirrigation and climate systems can be represented as event-based control problems where control actions will be calculated and performed when required by the system, for instance, when water is required by the crop or when ventilation must be closed due to changes in outside weather conditions. Furthermore, such as discussed above, with event-based control systems a new control signal is only generated when a change is detected in the system. That is, the control signal commutations are produced only when events occur. This fact is very important for the actuator life and from an economical point of view (reducing the use of electricity or fuel), especially in greenhouses where commonly actuators are composed by mechanical devices controlled by relays. Fig.5 is an example of real time control based system.

Figure - 5: An example of real time control based system

Fig.6 shows the generalized block diagram of event based control system using sensors and data acquisition system.

Figure- 6: Generalized block diagram of event based control system


RF is the wireless transmission of data by digital radio signals at a particular frequency. RF communication works by creating electromagnetic waves at a source and being able to send the electromagnetic waves at a particular destination. These electromagnetic waves travel through the air at near the speed of light. The advantages of a RF communication are its wireless feature so that the user needn't have to lay cable all over the green house. Cable is expensive, less flexible than RF coverage and is prone to damage. RF communication provides extensive hardware support for packet handling, data buffering, burst transmissions, clear channel assessment and link quality.


Low power consumption.

Integrated bit synchronizer.

Integrated IF and data filters.

High sensitivity (type -104dBm)

Programmable output power -20dBm~1dBm

Operation temperature range -40~+85 deg C

Operation voltage: 1.8~3.6 Volts.

Available frequency at : 2.4~2.483 GHz

High accuracy

Wide frequency bandwidth

Instantaneous wakeup mode.


Wireless alarm and security systems

AMR-automatic Meter Reading

Consumer Electronics

Industrial monitoring and control,

Wireless Game Controllers,

Wireless Audio/Keyboard/Mouse


In the proposed hardware, there would be two section master and slave. The slave part would contain the temperature and humidity sensor. The sensor would be connected to the AVR microcontroller. The RF transceiver would be connected to the AVR microcontroller which would wirelessly send the data to the master part. The master part would contain the RF transceiver which would receive the data and give to the microcontroller. The count would be displayed on the graphics LCD. The motor and DC fan would also be connected to the master board. These motor and DC fan would be accordingly controller based upon the relevant temperature and humidity condition. The major components of the proposed hardware are,

Microcontroller - AVR- Atmega 16, Atmega 32.

Compiler : AVR studio

Range - 150 meter

Master and Slave communication: 247 slaves.

Sensor: Temperature : LM35 and Humidity sensor


Fig.7 The block diagram of the proposed hardware


There are several features of Atmega microcontroller as given below which makes it an ideal choice for green house parameter monitoring.


High performance, low cost 8-bit micro-controller

RISC Architecture

Low power consumption

Supports RF protocol

Supports SPI and I2Câ„¢ Master and Slave modes

10-Bit, up to 11-Channel Analog-to-Digital Converter module (A/D)

Modes Supported: Run, Idle, Sleep

Operating frequency up to 20 MHz

Priority Levels for Interrupts, three External Interrupts

In-Circuit Serial Programmingâ„¢ (ICSPâ„¢) via two pins

In-Circuit Debug (ICD) via two pins

Wide operating voltage range: 2.0V to 5.5V


Typical Hardware Support includes,

Internal or External Oscillator/Clock

Brown Out Detector

One or more timer

Two or more PWM

One or more USART


Real time clock

10bit ADC

Analog Comparator

External interrupts

Pulse timing capture

V. WHY RF 2.4?

The important features given below in table I and table II make RF 2.4 an ideal choice for green house parameter monitoring.

Part    Status  Device   Frequency  Sensitivity Data Rate

Number Type Range   (Best) (dBm) kbps

CC2420 active -- -- -- --

CC2430 active System-on- chip 2.4GHz  -92 --

CC2431 active System-on- chip 2.4GHz  -92 --

CC2480A1 active Network Processor  2.4GHz  -92 250

CC2500 active Transceiver  2.4GHz  -104 500

CC2510F16 active System-on- chip 2.4GHz  -103 500

CC2510F32 active System-on- chip 2.4GHz  -103 500

Table I. Part number , status, device type, frequency range and sensitivity

Part    Frequency  Frequency Operating Voltage Description  Number (max) (MHz) (min) (MHz)   (max) (V)  

CC2431 2483.5 2400 3.6 System- on-

Chip solution for


802.15.4 Wireless

Sensor Network 

CC2480A1 2483.5 2400 3.6 Z-Accel 2.4 GHz

ZigBee Processor

CC2500 2483 2400 3.6 LowCost,LowPower

2.4 GHz RF



Wireless Apps

in the 2.4 GHz ISMB 

Table II. Part number, minimum and maximum frequency range, operating voltage and description

In nutshell, the advantages of RF 2.4 are,

Low power consumption.

Integrated data filters.

High sensitivity

Operation temperature range -40~+85 deg C

Available frequency at : 2.4~2.483 GHz- No certification

Required from government

High accuracy

Low cost


Integrated Development Environment

Write, Compile and Debug

Fully Symbolic Source-level Debugger

Configurable Memory Views (SRAM/EEPROM/Flash/Registers and I/O)

Trace Buffer and Trigger Control

 Extensive Program Flow Control Options

 Simulator Port Activity Logging and Pin Input Stimuli

 Language support:  C, Pascal, BASIC, and Assembly


The proposed system is a cheaper solution as compared to other similar technologies and hence suitable for the developing countries such as India.

Low cost and maintenance free sensors are proposed to be located to monitor environment. The system has several advantages in term of its compact size, low cost and high accuracy.

The proposed system considers design optimization and functional improvement of the system.

The reprogramming and flexibility are the main features.

The same system can be used to monitor industrial parameters.


[1] "Preliminary Infrastructure Development for Greenhouse Accounting of Malaysian Rainforest Using Wireless Sensor Network", European Journal of Scientific Research ISSN 1450-216X Vol.33 No.2 (2009), pp.249-260 © Euro Journals Publishing, Inc. 2009.

[2] "The Greenhouse Remote Monitoring System based on RCM2100", WANG Juan WANG Yan College of Mechanical and Electric Engineering, Agricultural University of Hebei, Baoding,071001, China [email protected]

[3] "A Study on the Greenhouse Auto Control System Based on Wireless Sensor Network", BeomJin Kang  Dae, Heon Park,  KyungRyung Cho,  ChangSun Shin,  SungEon Cho ,  JangWoo Park   IEEE, 22 December 2008.

[4] Anil Kumar Singh, " Precision farming" Water technology center, New Delhi

[5] Debashis Mandal and S. K. Ghosh, "Precision Farming"

[6] H. J. Hellebrand, H. Beuche, K.H. Dammer, "Precision Agriculture"

[7] S. M. Swinton and J. Lowenbergdeboer, "Precision Agriculture".

[8] Mahmoud Omid , "A Computer-Based Monitoring System to Maintain Optimum Air Temperature and Relative Humidity in Greenhouses"

[9] Teemu Ahonen, Reino Virrankoski and Mohammed Elmusrati, "Greenhouse Monitoring with Wireless Sensor Network"

[10] Andrzej Pawlowski, Jose Luis Guzman, Francisco Rodríguez, Manuel Berenguel, José Sánchez and Sebastián Dormido, "Simulation of Greenhouse Climate Monitoring and Control with Wireless Sensor Network and Event-Based Control"

[11] Candido, F. Cicirelli, A. Furfaro, and L. Nigro," Embedded real-time system for climate control in a complex greenhouse"