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

1606 Manish Bhimrao Giri and Dr. Ravi Singh Pippal

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.

REFERENCES

[1] L.M. Oliviera, J.J. rodrigues, “ Wirlesess Sensor Networks: A survey of

Environmental Monitoring”, JCM, 16(2),2011, pp.143-151

[2] N.Wang, N. Zhang and M. Wang, “ Wireless Sensors in agriculture and food

Industry- recent development and future perspective” , Computers and

Electronics in agriculture, 50(1), 2006, pp.1-14

[3] M. Srbinovska, C. Gavrovski, V. Dimcev, V. Krkoleva and V.Borozan,

“Environmental parameters monitoring in precision agriculture using wireless

sensor network”, Journal of Cleaner production, 80(1), 2015, pp.297-307

[4] Y.D. Kim, Y.M. Yang, W.S. Kang and D.K. Kim, “On the decision of beacon

based wireless sensor network for agricultural emergency monitoring system”,

Computer Standards and Interfaces, 36(2), 2014, pp.288-299

[5] Aqeel-ur-Rehman, Abu Zafar Abbasi, Noman Islam and Zubair Ahmed

Shaikh, “A review of wireless sensors and networks applications in

agriculture,” International Journal of Distributed Sensor Networks, 36, 2014,

pp. 263-270

[6] Zenglin Zhang , Pute Wu, Wenting Han , “Advanced Wireless Sensor

Networks for Soil Property Monitoring”, Journal of Information and

Computational Science, 9(14), 2012, pp.4141-4147

[7] M. Benghanem, “ A low cost wireless data acquisition system for weather

Station monitoring” , Renewable Energy, 35(4), 2010, pp. 862-782

(Retracted)

[8] K. manikadan, S. Rajaram, “ Automatic Monitoring System for Precision

Agriculture Based on wireless Sensor Network”, International Journal of

Science, Engineering and Computer Technology, 6(6), 2016, pp.208-214

[9] K.P. Ferentinos, N. Katsaulas, A. Tzounis et.al, “ Wireless Senso Network for

green House and Plant condition Assessment”, Biosystems Engineering,

A Pervasive system for optimizing water utilization in Agriculture… 1615

153(1), 2017, pp.70-78

[10] R. Bogue, “ Sensors key to Advance in precision Agriculture”, Sensor

Review, 37(1), 2017, pp.1-6

[11] W. Robert, A.Coates, J.Michael, A. Delwiche, Alan Broad Mark Holler b

Wireless sensor network with irrigation valve control. Computers and

Electronics in Agriculture 96,2013, pp.13-22

[12] R.D.Baruah, R.M.Bhagat, Saikia M., Saikia A., Pathak L.B., Gupta N.1,

Sengupta Lahari and Bhattacharyya, “ Using wireless sensor network for

monitoring growing environment of Tea in North- East India”, 8(2), 2013,

pp.325-334

[13] A. Paventhan, Sai Krishna Allu, Sameer Barve, V. Gayathri and N. Mohan Ram, “Soil prooerty monitoring using6LoWPAN-enable wireless sensor network”, Agro-Informatics and Precision Agriculture 2012 , pp. 277-282

[14] Aurelio Cano, José Luís Anon, Candid Reig, Cristina Millán-Scheiding

,Ernesto López-Baeza, “ Automated Soil Moisture Monitoring Wireless

Sensor Network for Long-Term Cal/Val Applications”, 4, 2012, pp.202-209

[15] S.U. Ufoaroh, K.N. Ogbu, S.L. Ayinla and P.C. Obioma, “ Automated

Irrigation system (water and Herbicides) Interfaced with GSM Remote

monitoring system”, International Journal of Electrical and

Telecommunication System research, 8(8), 2016, pp.98-108

[16] Ismail Ahmedy, Hishem A. Shehadeh and Mohd Yamini Idna Idris, “An

Estimation of QoS for Classified Based Approach and Non classified Based

Approach of Wireless Agriculture Monitoring Network Using a Network

Model “, Wireless communication and Mobile Computing, 2017, pp. 1-14

[17] Saurabh Suradhaniwar, Suryakant A. Sawan, Mrunalini Badnakhe, Surya S.

Durbha, J. Adinarayana, “An interoperable wireless sensor network platform

for spatiotemporal soil moisture and soil temperature estimation”, Agro

Geoinformatcs IEEE, 2016

[18] http://www.agriinfo.in/default.aspx?page=topic&superid=7&topicid=17

[19] https://www.mpkv.ac.in

[20] https://www.msamb.com/Projects/ProjectConsultancyDivision

[21] Drip Irrigation Design Guidelines, https:// www.irrigationtutorials.com/drip-

irrigation-design-guidelines-basics-of-measurements-parts-and-more/

1616 Manish Bhimrao Giri and Dr. Ravi Singh Pippal