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International Journal of Applied Environmental Sciences
ISSN 0973-6077 Volume 12, Number 8 (2017), pp. 1605-1615
© Research India Publications
http://www.ripublication.com
A Pervasive system for optimizing water utilization in
Agriculture using wireless Sensor Networks
Manish Bhimrao Giri
Department of Computer Science and Engineering RKDF University, Bhopal, India
Dr. Ravi Singh Pippal
Department of Computer Science and Engineering RKDF University, Bhopal, India.
Abstract
Wireless Sensor Networks (WSN) are playing vital role in precision
agriculture. In the current era, many sensor variants are being used in the
agriculture sector for different process, production and precision monitoring
system. Apart from this, Wireless sensor Networks are used for decision
support systems in agriculture for various processes. This paper provides an
insight of an automated drip irrigation decision support system for optimum
utilization of available water to be distributed over a specified cultivation land
with multiple crops. This system works with a network of wireless Sensor for
data acquisition. All sensor nodes collect data from various geospatial
locations and send it to sink node to provide decision support for water
distribution. The main focus is to reduce the cost of a working system using
less number of the Sensor node in comparison with other methods studied for
a survey. Many novel methods are used for automated drip Irrigation. The
presented system narrows the focus on soil moisture data collection between
two sensor nodes using piece wise Interpolation. A multi hop transmission is
used in data transmission from a source node to sink node.
Keywords: Wireless Sensor Network, Decision Support system, precision on
agriculture, Multi hoping transmission, Drip Irrigation
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I. INTRODUCTION
The advent of Wireless Sensor network has improved the wireless resource utilization
in agriculture sector. Use of wireless sensor network for environmental parameter
monitoring has given a new methodology for framers to increase their farm
production by optimum utilization of available resources. The versatile environmental
parameters, geographical location and uncertain climatic conditions has made farmers
decisions more cumbersome and tedious [1]-[3]. There is considerable growth of
parameter monitor using the sensor in previous few years and hence agriculture sector
is in transformation phase from conventional to modern practices [4].
A wireless sensor network is a ubiquitous system of sensors to monitor physical or
environmental conditions, like soil moisture, soil properties such as texture, color, the
capacity of water content, temperature, Humidity and much more. To concomitantly
pass parameter values through the main location ZigBee wireless communication
technology (IEEE 802.15.4) is adapted over other technologies for the implementation
sensor nodes and its communication. Low cost and low power consumption features
of ZigBee, so it is widely used [5]. The wireless sensor networks can provide an easy
access of real-time field data to farmers for decision support [6]. The agriculture
sector is a very potential field where WSN can be used with ease.
The presented system uses soil moisture property as a parameter to be transmitted
over a network and it will be used to verify conversions of current needs of crop and
according to the decision are made. The decision is made through automated process
based on a comparison of parameter values between pre-set threshold and sensor
values transmitted by sensor nodes which are deployed at the different point in the
cultivating land. The scope of the project can be extended to green house
environmental parameter, Industrial use, Power plants and Chemical plants. Here it is
limited to agriculture sector for monitoring process and decision support system.
II. LITERATURE SURVEY
Wireless Sensor Network is formed by distributed sensor nodes where each node
consists of various sensors to read physical phenomenon such as Light, Heat, Soil
Moisture, Temperature, Humidity, pressure. These sensor nodes are regarded as
revolutionary data collection methods to transmit information at geographical apart
locations. It has great reliability and efficiency of a working system. United States
Military pioneered the use of Sensor node networks in Civil War II to detect
submarines and air defense. It was called Distributed sensor networks which were
used to process data collaboratively [7]-[10]. Previously, many researchers worked on
WSN application in precision Agriculture. Following are few remarkable works done
by researchers.
A Pervasive system for optimizing water utilization in Agriculture… 1607
Robert W. has presented a novel method for valve control method. He has developed
valve control hardware and software. These hardware and software modules are
compatible with commercial WSN nodes. The valve control units are available to
deploy in fields to control and monitor different necessary parameter. The valve relay
system appends sensor node firmware, actuator hardware, an internet gateway with
control, communication and web consolidation [11]. Bhagat focused on drought
vigilance in tea plantations. He worked on environmental parameters such
temperature, humidity and soil water content. These parameter values are transmitted
remotely to the sink node or server. Based on these sensor readings, server proffers
the idiosyncrasy of working adherence and reliability in the system [12]
Paventhan worked on IEEE standard used in wireless data transmission. The IEEE
802.15.4 (IEEE Std 802.15.4, 2006) Low-rate Wireless Personal Area Networks
standard is anticipated at applications with limited power and tolerable throughput
exigency. The Internet Protocol (IP) is adapted over Ethernet links that produce
colossal good put. He ensures transmission of IPv6 packet over LoW PAN links.
There were many challenges of resource constraints. However, concludes the benefits
in enabling IPv6 over 802.15.4 links such as (1) large IPv6 address space and stateless
auto configuration (2) Network vigilance (3) Feasibility of application layer protocols
(4) seamless and end-to-end integration with a connected network. IETF 6LoWPAN
working group has defined RFC 4944 specification (6LoWPAN, 2012) to efficiently
move IPv6 data grams over IEEE 802.15.4 links. [13]
Aurelio Cano developed a network abide with a number of automatic measurement
stations, stereo typically placed soil congruity and land utilization criteria. In this
system, a base station performs acquisition, conditioning, and communication through
nodes. To achieve uninterrupted power supply, the solar panel was used to activate
sensor nodes. The gathered parameter values are then routed through long radio links
to cover a larger spatial range. The presented system used computer system connected
to the web in contemplation to control the entire network, to deposit information, and
to access remotely monitored real-time data [14]
Ufoaroh worked on GSM based sprinkler irrigation system to monitor water
distribution in the farm field. He uses soil moisture and soil temperature parameters to
find current conditions of the soil. Depending on the sensor reading, decisions are
made for the need for irrigation. A pumping mechanism is used to water the
cultivation land as per requirements of the crops. Water levels are monitored and
transmitted to farmers through GSM based system [15]
Ahemady, worked on challenging issue of implementing the agriculture monitoring
network at colossal and large distributed area. He used multi hop network to implant
communication between source and destination. The network sequentially monitors
the crops without sensitivity classification to safeguard a high quality of service
1608 Manish Bhimrao Giri and Dr. Ravi Singh Pippal
(QoS). For this, he monitored the crops based on respective sensitivity conditions as
well as the crops with higher sensitivity. But less sensitive crops are tracked
occasionally. This access selects a set of nodes over all the nodes in the network
leading the reduction in power consumption and network delay. He preferred
classified based approach for comparing nonclassified method in two scenarios such
as the back off periods change and numbers of nodes change [16]
Suradhaniwar, designed an interoperable sensing platform that accedes with Open
Geospatial Consortium (OGC) Sensor Web Enablement (SWE) framework. He
Proposed a system with sensors to estimate point-based soil moisture and temperature
observation at different depths of 15, 30, 45 and 60cm each. The research was carried
out 2016 in the study area. He concludes with perception for soil moisture and soil
temperature delineation at different depths. He analyzed the possibility of deploying
dense interoperable sensor networks to capture spatiotemporal changeability of soil
moisture over amalgamate landscapes [17]
III. SYSTEM ARCHITECTURE
The core part is on enthralling self-controlled irrigation system using Piece wise
Interpolation method. This system is promoting perpetual valve timings of drips fixed
on sensor learning from the sensor nodes and it’s tuned timings. A microcontroller is
used to store data from nodes. The gathered analog values are persuaded to digital
values using ADC converter. The system approves the numerated water distribution.
The settlement is made on the basis of readings compiled from sensor nodes. Once the
data is collected then Piecewise Interpolation is adapted to vaticinate watering plan
for maximum land cultivation and maximum profit sharing. A microcontroller
initializes the statute to different pervasive devices such as water pumping motor,
valve, to switch ON or OFF. A multi hoping method is used to communicate with the
master node. The master node is connected to the server. Server compiles data for
computing decisions.
The three parameter values are verified such as temperature, soil moisture, and light
intensity. The farm field is divided into small parts and one node is positioned in each
part. Each sensor node is carried LM35 for temperature reading and Tensiometer for
volumetric soil moisture contents. The sensors are spread out at 6-8 cm underneath
the soil level. As soon as moisture level ambits the threshold value then it is
communicated to a microcontroller to share a decision over, valve switch ON or OFF.
A. Interpolation Method
The interpolation method is used to predict intermediate values between two sensor
A Pervasive system for optimizing water utilization in Agriculture… 1609
nodes deployed at geographically apart locations. In the presented system, sensor
nodes are used to read the soil moisture and temperature data. The collected data is
transmitted to the server. As the sensor nodes are deployed at different points,
intermediate values need to be calculated. Interpolation is the most suitable method
for measuring these values.
B. Algorithm
1. Accept the value of x.
2. Accept the value of y.
3. find local variable.
4. S=x-x(k) ,Where k=0,1,2...
5. Find first divided difference Delta(k) δ (k) =(y (k+1) − y( k)) / (x (k+1) − x(k))
6. find the interpolant
L(x)= y(k) + (x − x(k))(y(k+1)−y(k))/(x(k+1)−x(k)) = y(k)+ δ(k) .
IV. RESULT AND DISCUSSION
In the presented system, a volumetric water content (VWC) is measured for decision
support. This system collects the analog values from the required sensor node. These
analog values are converted to digital values and then transmitted for the further
computational process. The Volumetric water content is based on soil properties such
as soil texture, soil partiole density, Bulk density, temperature etc but in a presented
system only one moisture holding capacity is considered. For VWC a standard
formula is used as follows
VWC = 1.17 * 10-9 * ADC3 – 3.95 * 10-6 * ADC2 + 4.90 * 10-3 * ADC – 1.92
(1)
The Volumetric water contents are established on various soil assets such as soil
texture, soil partiole density, Bulk density, temperature etc, but in this system only
moisture holding capacity is mediated.
All computational part is considered as per the cultivation land used in experiment
sand then it is been converted day wise calculations. For all computational part
following standard definitions are used to convert necessary conversions as and when
needed
1610 Manish Bhimrao Giri and Dr. Ravi Singh Pippal
1. Cubic foot: A volume of water equal to that of a cube 1 foot in length, 1 foot
in breadth and 1 cubic foot in thickness. One cubic foot of water = 28.37 liters
or 6.23 gallons or 0.0283 cubic meters or 0.028 ton[18]
2. Acre inch: the volume of water necessary to cover one-acre (43,560 sq. feet)
surface to a depth of one inch. One hectare inch = 3630 cubic feet or 101
ton.[18]
Sr. No. Depth of Soil
Layer ( cm )
Moisture % on oven dry basis Apparent specific
gravity g/cc Field Capacity Actual
1 0 - 15 25.0 16.4 1.39
2 15 - 30 24.0 17.8 1.47
3 30 - 60 22.3 19.2 1.51
4 60 - 90 22.2 20.5 1.53
A. Hardware part
Fig.1 Sensor nodes used in experiment
A Pervasive system for optimizing water utilization in Agriculture… 1611
B. Data Collection
Fig.2 Sensor node data collection on terminal
Fig.3 Collected data used for Decision support system
For actual result analysis, water consumption and power consumption parameters are
considered. For the result analysis and comparison with experimental values, the
standard agriculture environmental parameters are considered from various
agriculture universities in and around Pune region, such as College of Agriculture and
biotechnology, Mahatma Phule krishi Vidyapeeth, Pune and Maharashtra State
Agriculture Marketing Board (MSAMB). All standard values of soil moisture
1612 Manish Bhimrao Giri and Dr. Ravi Singh Pippal
requirement, water utilization and power consumption in water distribution, compared
with actual experimental results [19]-[20]
Table 1. Consumption of water and power, day wise plot for one Acre cultivation
land up to 12 cm without Piecewise Interpolation
Time Drip Irrigation without Piecewise interpolation
Water in Lit. Power in watt
9.00-10.00 121.5 243
10:30-11:30 121.5 243
12:00-12:30 121.5 243
12:45-01:50 108.33 216.66
02:00-03:00 121.5 243
03:10-04:10 121.5 243
04:15-05:15 100 200
05:20-06:20 121.5 243
Fig.4 Consumption of water and power, day wise plot for one Acre cultivation land
up to 12 cm without Piecewise Interpolation
0
50
100
150
200
250
300
Water in Lit.
Power in watt
A Pervasive system for optimizing water utilization in Agriculture… 1613
Table 2. Consumption of water and power, day wise plot for one Acre cultivation
land up to 12 cm with Piecewise Interpolation
Time Drip Irrigation with Piecewise interpolation
Water in Lit. Power in watt
9.00-10.00 95.34 187.4
10:30-11:30 92.67 183.3
12:00-12:30 90.23 179.1
12:45-01:50 93.56 184.5
02:00-03:00 92.87 183.3
03:10-04:10 91.39 182.8
04:15-05:15 73.11 167.7
05:20-06:20 92.14 182.9
Fig.5 Consumption of water and power, day wise plot for one Acre cultivation land
up to 12 cm without Piecewise Interpolation
V. CONCLUSION
By estimating the two data tables, it is ascertained that the water consumption with
and without Piecewise Interpolation has a huge difference. 937.33 – 721.31 = 216.02
which is almost 24% water saving in overall water consumption. In power
consumption 1874.66 -1451 = 423.66 which is again almost 23 % power consumption
0
20
40
60
80
100
120
140
160
180
200
Water in Lit.
Power in watt
1614 Manish Bhimrao Giri and Dr. Ravi Singh Pippal
saving. All the values are calculated based on one hour continuous running 10 HP (1
H.P. = 745.7 W Energy consumed = Power × Time) water pump and consuming
distributing water through 6 mm pipe [21]
ACKNOWLEDGEMENT
I herby take the opportunity to say thanks for all valuable support from RKDF,
University, Bhopal. Apart from this, I am really grateful to MSAMB, Pune and other
Agriculture colleges for their support for data calculations and analysis.
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1616 Manish Bhimrao Giri and Dr. Ravi Singh Pippal