Proc. Int. Conf. on Computational Intelligence and Information Technology, CIIT
Inegrated Wireless Sensor Network for Smart Sesame
Farming in India
Rekha P, Lekshmi G.S and Maneesha V.Ramesh
Amrita Center for Wireless Networks and Applications,
AMRITA Vishwa Vidyapeetham (Amrita University)
Kerala, India-690525
{ rekhap,lekshmigs,maneesha }@am.amrita.edu
Abstract. Wireless Sensor Network technology has been used for continuous
monitoring of farm lands to increase the agriculture yield. This research work
proposes the design of a wireless sensor network system for continuous monitoring
of sesame cultivation. It proposes a design of an integrated wireless sensor network
that senses various agriculture parameters such as soil moisture, soil pH and soil
temperature. The research has developed several decision algorithms to alert the
farmers, the field conditions and the remedial measures to attain higher yield. The
prototype system has been tested and the results are given along with this paper.
This work presents a cost effective technology to increase production and improve
agriculture yield for sesame farming in India.
Keywords: Wireless Sensor Network,Agriculture,Sesame,Short Message Service
1 Introduction
Agriculture is one of the strongholds of the Indian economy and accounts for 14.6 percent
of the country's gross domestic product (GDP) in 2009-10, and 10.23 percent
(provisional) of the total exports. Furthermore, the sector provided employment to 58.2
per cent of the work force.
Wireless Sensor Network technology is one of the effective technologies used for
continuous monitoring of environmental parameters for real-time detection of specific
phenomena. The major goal of WSN technology is to sense agriculture characteristics and
advice farmers to properly grow and treat sesame crops.
The system deploys a group of WSN nodes deployed in the sesame field for sensing
agricultural parameters and the RF communication of WSN node is used to transmit the
measured data to base station. Base station is connected to a decision support system.
Based on the sensed parameters and the optimum values, the decision support system will
generate an appropriate message for farmers and the subsystem that delivers SMS will
send alert message to farmers.
The existing system in India for monitoring sesame crop production is manual. Soil
sample is physically collected from different parts of the sesame farm field and soil
testing is conducted in agriculture department’s soil testing labs .The tested values are
© Elsevier, 2012
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compared with the optimum values required for effective cultivation of sesame crops. The
results are sent to farmers through post cards.
An integrated wireless sensor network is proposed for better crop yield.WSN node
plays an important role in measuring and transmitting the valuable data from agriculture
field to the base station and decision making subsystem.
The next few pages describe the model of an integrated wireless sensor network and its
application in smart farming methods.
2 Related Works
The concept of smart-farming has been around for some time now. Carlos Seridio, in
2001[1] suggested flexible hardware and software architecture to manage green house
farming based on network and internetwork. J. Burrell [2] performed an experiment
about the usefulness and usability of wireless sensor networks for rain-fed agriculture.
He could clearly identify usefulness of providing finer-grained environmental data for
applied research in this field.[3] Jer Hayes presented a paper on the development and
testing of a low cost wireless chemical sensor network(WCSN) for monitoring toxic
gases in the environment. Anurag D[4] developed an architecture for precision
agriculture based on wireless sensor networks. This architecture consisted of different
components like intelligent nodes with sensors, the wireless mesh network for
communication and the design of routing algorithm, control and actuation. J. Nelson
Rosario [6] proposes a state of art wireless sensor technology in agriculture, which can
show the path to the rural farming community to replace some of the traditional
techniques. In this project, the sensor motes have several external sensors namely leaf
wetness, soil moisture, soil pH, atmospheric pressure sensors attached to it. Based on the
value of soil moisture sensor the mote triggers the water sprinkler during the period of
water scarcity. Once the field is sprinkled with adequate water, the water sprinkler is
switched off. Hereby water can be conserved. [7]Doan Minh Chung proposed in his
reports a similar investigation of passive microwave remote sensing on water-rice fields
in Vietnam. A simple model for emission of water-rice media is presented. A series of
microwave radiometry field experiments carried out by scientists of STAC, Institute of
Physics, NCST of Vietnam is described. Muhamad Azman Miskam[5] in his work
proposed a new architecture design to cater the most important and critical issue in
wireless sensor network that is power consumption. The design will attempt a sensor
node board systems consist of a low power microcontroller so called nano-Watt
technology, low-power semiconductor based or MEMS sensors, Zigbee™ IEEE
802.15.4 wireless transceiver and solar energy source with optimal power management
system. J. Panchard [8] made a study from the point-of-view of distributed cognition, the
goal being to understand how a new tool fits in the mental model that agriculture
scientists have of their own field of work, and how it may influence or enhance their
mental representations and work-processes.
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3 Sensors for Monitoring Sesame Farming
The productivity of sesame farming is heavily dependent on climatic conditions and soil
conditions. The sesame crop is intolerant to excess water and the amount of water
needed for sesame is different at different stages of the plant growth. Sesame crop will
not survive if there is too much water. The crops need moisture for approximately three
to five days. A heavy pre-irrigation is what they need the most. Excess moisture will
shorten germination and seedling stages but will lengthen the rest of the stages. Hence
continuous monitoring of soil moisture is very important for the better productivity from
sesame crop cultivation. Hence the proposed system should be integrated with soil
moisture sensor that will provide the capability to continuously monitor the soil moisture
and provide the required advice Sesame crop is heat tolerant, and its preferred
temperature value is 60F. The productivity of the crop gets decreased if the temperature
reduces. Hence it is necessary to continuously monitor the soil temperature using a soil
temperature sensor.
Sesame plants need slightly acidic to alkaline soils and the preferred soil pH is
between 5 and 8 with moderate fertility. To monitor the variations in the pH value, the
proposed system has to integrate pH sensors for continuous monitoring of acid levels in
the sesame farm land.
Other climatic conditions such as excessive rainfall, wind etc will also affect the
sesame crop production. Hence it is essential to have a weather station to continuously
monitor the rainfall rate, wind, atmospheric temperature etc.
Thus the proposed system will be integrated with different sensors such as soil
moisture sensor, soil temperature sensor, pH sensor, weather station etc.
3.1 Sensing Coverage
The proposed monitoring system contains heterogeneous sensors such as moisture sensor
(Fig 2), pH sensor, soil temperature sensor and weather station. The sensor coverage for
each of the sensor is different.
The sensing coverage is also dependent on the properties of soil, sesame farming
patterns, infiltration rate, and the expected rainfall rate. Considering the above
mentioned cases, the sensing coverage for each sensor is determined, which will help to
determine the optimal number of sensors needed for continuous monitoring of sesame
crop production.
Let AxA is the area of the sub region and RS is the sensing coverage of the sensors.
Now if we want to introduce an m-coverage for area, choose the number of lower level
nodes, NL, as
A = m.NL.RS
(1)
Sensor Placement
For the design of an efficient, fault tolerant, m-covered and k-connected network, we are
adopting the following placement strategy.
1. Inside the sub area, place the clusters equidistance from each other
2. Place the lower level nodes equidistant from all clusters and equidistant from each
other.
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We are using a region wise clustering method for sensing coverage.Incorder to get
network connectivity we are dividing the crop filed into different regions based on the
ratio of
N: 5*N
(2)
In order for maintain better network connectivity one acre land can be divided to 5 sub
regions. The network coverage of each mote is 30 to 35m.Each sub region is provided
with 4 sensor nodes. One node will act as the cluster head.
Connectivity can be modeled as a graph G(V,E) where V is the set of vertices and E is
the set of edges. This graph is said to be k connected if there are at least k disjoint paths
between every pair of nodes u ,v ϵ V. Connectivity is a measure of fault tolerance.
Connectivity of a network can be expresses as[9]
μ( R ) =NπR2 / A
(3)
R- Radius of Transmission
A-Area
N-Number of nodes
Klunrock and Silverster[10] have shown that when connectivity
μ( R ) reaches 6 nodes, the probability that a node is connected tends to 1, the network
forms a connected graph.
From these it is observed that the optimum number of nodes needed to get a
completely connected network is
N=6A/πR2
(4)
4 Wireless Sensor Network Architecture
Multiple wireless sensor nodes connected to the heterogeneous sensors such as soil
moisture sensor, pH sensor, soil temperature sensor, weather station etc are deployed
with respect to the sensing coverage requirement derived in the previous section. These
wireless sensor nodes will collect the data continuously from these sensors and transmit
it wirelessly to base station.
The data from the base station is continuously transmitted to the decision support
system. The data will be analyzed using the different decision algorithms. If the decision
values are less than the threshold levels, warning messages (in the form of SMS) will be
sent to the farmers. Fig-1 depicts the architecture design of the system.
The proposed system has the following modules:
• Wireless Sensor Array Network: Wireless sensor array senses agriculture
parameters such as soil moisture, soil temperature, soil pH value, rainfall rate,
wind rate etc, collect and transfer the data from crop field to control station
• Decision Support System: The received from the base station will analyzed
based the different decision algorithms and its result will be compared with the
data available from the knowledge base
• Knowledge Base System: The knowledge base system collects and saves the
relevant information about the parameters, its thresholds, soil properties etc
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•
which will help to achieve higher yield. These values from the knowledge base
will be compared with the results from the decision support system
Fertilizer Recommendation: Will be performed as per the results received from
the decision support system. The warnings will be disseminated using alarms
and SMS.
Fig 1 –Architecture Design
Sensing agricultural parameters
To improve sesame crop yield three agriculture parameters are chosen such as optimum
irrigation, soil pH and soil temperature. For sensing the soil moisture EC-5 soil moisture
sensor is used. The sensors are connected to the MICAz node. The pH sensor used in the
application needs a 3-12 V as operating voltage with 1.5 mA which can be drawn from
the 2xAA batteries in the sensor node. The sensor has an output used to indicate the
obtained soil pH value. Temperature Sensor will obtain the value of soil temperature.
Decision Support Software and alert message sending
Based on the collected data from sensor nodes, the decision support system will
determine the amount of water and the type of fertilizer required for the growth of
sesame crop. Periodic alerts of fertilizer and water supply will be delivered to farmers’
through SMS.
•
Data Acquisition Algorithm
Lower level node
Data Acquisition ()
{
1. Define the packet format
SourceNodeId
ClusterHeadId
RegionId
SensorpHdata
SensorMoistData
SensorTempData
2. Set the sampling rates of sensors timeph,timemois,timetemp
3. Start sensor data acquisition
4. If(val(phsensor)<Optphlevel1 and val(phsensor)>optphlevel2)
Formation of packet
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Transmit packet to cluster head
Reset Buffer
5. If val(TempSensor<OptTemp)
Formation of packet
Transmit packet to cluster head
Reset Buffer
6. If val(MoistSensor> OptMoist)
Formation of packet
Transmit packet to cluster head
Reset Buffer
}
•
Data Aggregation Algorithm
Cluster Head
Data Aggregation ()
1. Define the packet structure and set the power level as P1
2. Rfpowerlevellowerlevel=Call PowerOptimization(lowerlevel)
3. Rfpowerlevelsink= Call PowerOptimization(sink node)
4. Start Data Acquisition from lower level nodes
5. Store data in buffer
6. Sensedata1=rms(SensorphData)
7. Sensedata2=rms(SensorMoistData)
8. Sensedata3=rms(SensorTempData)
9. Formation of packet
10. Transmit packet to sinknode
11. Reset buffer
PowerOptimization(nodetype)
{
1. Initialize the RF power levels p1,p2,p3,p4,p5
2. Set i=1
3. Begin
do
{
When receive event happens
Check the Acknowledgement.RSSI from lower level nodes for pi
4. If it is high
Set the RF power level as pi
Exit
Else
Repeat the step 3 for pi= pi+1
}
Transmit the data with power level pi
}
•
Decision making Algorithm
Sink Node
Switch case (sensor type)
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Case: Moisture sensor
Switch case (region id)
Case region n:
Case region n+1:
Sensedata1<threshold
Send(Message to switch on Springer machine)
Case phSensor:
If(Sensedata2<threshold)
Send message for putting fertilizer
Case soiltemperature:
If(Sensedata3<threshold)
Send message to farmers to take appropriate action
Fig 2- EC 5 Soil moisture sensor in crop field
5 Testing
The application is developed using Processor AtmelATMega128@8MHz with Frequency
band 2400MHz -2483.5 MHz and implemented using nesC for the TinyOS open source
operating system that runs on MICAz. The sensed values are compared with the
optimum values (Table 1) and the decision is sent to farmers in the form of SMS alert.
Table-1 Optimum values of agricultural parameters
Soil Properties
Optimum Value for Sesame
Crop
Soil Temperature
Soil pH
Soil Moisture
Greater than 60.6oF
Greater than 5.6
12 to16 inches(water usage)
Fig 3- Experimental Setup
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Application is tested on different soil samples (Fig 3). The values obtained from
sensor using XSniffer and data analysis is represented in Fig 4 .When the sensed value
crosses the optimum range SMS alert is sent to the farmers with appropriate messages
which help them to scientifically monitor the sesame crop.
Fig 4 Sensor output and data analysis
6 Conclusions
In this paper we have presented a real time monitoring system for measuring the
agricultural parameters for improving the cultivation of sesame crop. We have
implemented decision support system that disseminates proper warnings and suggestions
to farmers based on the sensed agriculture parameters. The system also addresses power
optimization techniques for enhancing the network life time. With the aid of GUI, minor
modifications in the knowledgebase and the integration of new sensors will enable the
proposed system to suit for monitoring other crops.
Acknowledgments. We would like to express our immense gratitude to our beloved
Chancellor Sri.Mata Amritanandamayi Devi for providing excellent motivation and
inspiration that facilitated this research.
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