IRJET- Iot Based Intelligent Management for Agricultural Process using Raspberry PI

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 03 | Mar-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 3971
IoT BASED INTELLIGENT MANAGEMENT FOR AGRICULTURAL PROCESS
USING RASPBERRY PI
Mrs.S.Preethi1, R.Anbumalar2, T.Gokul3 , R.Karthik4, S.Mohanapriya 5
1Assistant Professor, Dept of ECE, Dr.NGP Institute of technology, Tamilnadu, India
2,3,4,5 Student , Dept of ECE, Dr.NGP Institute of technology, Tamilnadu, India
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Abstract - Since now a day the system of irrigation
practice is not based on maximizing output for plants when
compared to traditional mode of practice so large scale
changes are needed to modernize production through
raspberry pi. The combination of wireless sensors and
raspberry pi through web server and fertilizer sprayer is
proposed to support the smart agriculture. The sensors are
connected to web server via raspberry pi controls the field
activity. The sensors senses each activity happening in the
field and upload in web server. The farmer will come to
know about the field scenario through messages and can
smartly do watering and spraying fertilizer to the field in
very efficient manner without overdoing it and quality of
the crops is saved.
Key Words: Raspberry pi, Wireless Sensors, Web
Server, Spray Fertilizer, Smart Agriculture.
1. INTRODUCTION
Water is an essential element in the world. More than
70% of the world is covered with water. Out of those
water only little more than 3% is pure water. So saving of
water is important. The irrigation process of agriculture
uses sometimes more water than needed. The accurate
use of water in irrigated agriculture is very difficult
process. More than 50% of the water is wasted.
In previous years, with the help of sensors water
pumping process in agriculture become more accurate
with less wastage of water. With GSM technology and also
with fuzzy logic field owner will come to know about their
field condition. Henceforth, owner can monitor their field
and control it automatically. But with GSM and fuzzy logic
transmission of field condition to owner is not very fast.
The owner will only know the information very late. The
information from sensor will transmitted from one block
to others previous projects contains 3 to 4 blocks so if any
block get damaged then the transmission will be stopped
and identification of damaged block is difficult.
In the proposed system of smart agriculture uses LAN
technology by means of which sensors, controllers and
computers are combined to connect people and things. So
that it leads to higher production rate and to use the
resources correctly. So this system is dedicated for the less
wastage of water and automatic spraying of fertilizers and
also transmits information to field owner easily.
2. RELATED WORKS
F.Viani..,[1]The proposal of the paper is to design efficient
and low cost decision support system which is to be used
in agricultural process. So that the water usage can be
reduced and improvising water source can be achieved.
The fuzzy logic was taken for human work and crop
condition. Thus it paves the way for the smart irrigation
system.
Mengzhen Kang..,[2] The proposal of this paper is to
present 3 steps toward the parallel management of plant.
This approach can update the expert system by adding
learning ability and adaption of knowledge database
according to descriptive and predictive model
computational experiments can be conducted on different
of QTLs on finial yield. Such kind of theoretical study has
which can give promising prescription that may guide real
breeding.
Wanjong Song..,[3]proposes that by using temperature
sensor with Time-Domain Delta-Sigma converter a higher
resolution rate can be achieved. To obtain the accuracy a
reference clock is used as an external source in place of
temperature independent inverter cells. The result
obtained from the delta-sigma modulator is immediately
converted into digital result. Such that the high resolution
difficulties can be overcome in the mobile applications.
Alex Martinez-Agiree..,[4]roughness of the soil affects
the microwave scattering. So this paper was proposed to
find the roughness scale used for the soil in agriculture in
back scattering process. Eight various types of roughness
were processed and checked. As a result medium-
frequency roughness components were observed as a
needed one for the soil.
Francisco Yandun..,[5]to overcome the food demand in
near future the agricultural production needs to be
doubled. These goals are interrupted due to climate, cost,
land degradation, no availability of land for farming etc.
This paper presents the estimation of phenotyping
variables with respective measurements like plant
structure identification, plant/fruit detection and plant
physiology measurement.
D.Herrera.,[6]an autonomous vehicle was designed for
agricultural task. Here emphasis was made in lateral and
rotational dynamics. Quadricycle has been modified and

adopted to work in independent manner. Simulation
proofs and experimentation with real vehicle that allowing
guaranteeing the performance of the model.
Pratap Tokekar..[7]this paper focuses the use of robots
in agricultural field. It predicts the crop condition by
means of the data collected from sensors and ground. The
time taken to collect information from ground can be
reduced by using sampling TNSPV for measurements.
Along with time deduction, energy limitation and nitrogen
map of a plot can also be achieved.
Ahmad Ashraf Abdul Halim..,[8]Greenhouse is the mail
stone for the producing high-value vegetables and fruits.
To maintain its growth and production it is necessary to
monitor the requirement for each phase of plant. The
system monitors and manages each phase of the plant
cycle. In the past few years temperature, humidity and
other requirements of the greenhouse is monitored
through auto data collection or through manual source
which may lead to same errors.
Abdul MatinHowlader.,[9]to overcome the energy
demand in future smart grid is the only solution. In this a
wind farm is combined with smart house. Fluctuations in
wind turbine occur as output power. Surplus power
obtained from smart house is transmitted again to power
grid so that the extra power obtained can be exchanged for
some profit.
Joaquin Gutierrez.,[10]In the water limited zones an
automated agricultural process was designed to pave way
for water usage in the plants. Here sensors and
microcontroller based algorithm are being used to control
water quantity. This system was tested and 90% result
was obtained when compared to traditional agricultural in
water conservative areas of the field agricultural system.
Nicolas Baghdadi..,[11]The surface roughness of the soil
was analyzed in this paper by using the synthetic aperture
radar(SAR) over bare agricultural areas. From that IEM
model in polar metric version should be checked by using
the database obtained. Though there may 2 different
variations it can be detected easily.
Suhinthan Maheshwararajah..,[12]The disconnected
orphaned nodes are identified in this paper. This occurs
due to failure of receiving address from the parental node.
It may affect the control system of automated irrigation
system. In general it cannot be avoided due to the
presence of noise. To compensate this issue optimal
restoration is adopted to restore the disconnected nodes
to the networks and by satisfying constraints in farming
areas.
Paul A. Hammond..,[13]designed an system-on-chip pH
meter to communicate and transmit the pH data obtained
by collecting and processing it through microcontroller
unit. ISFET is used in the analog system. Component re-
use is the major advantage of this design which is
connected to computer and generates 37bits/pH.
Juan F.Posada..,[14]describes an automated irrigation
system to monitor the characteristics of various frequency
response of the soil under different conditions. This
system was proposed to overcome the issue of water
scarcity among the increasing population. As a result
empirical model for sensors is obtained from the
measured data’s.
3. EXISTING SYSTEM
The usage of WSN system in agriculture were used by
many researchers. After that sensors have been used to
improve the environmental monitoring in agriculture and
it supports farmers for the irrigation.
The concentration of fresh water level management has
been increased in the world. And with the help of WSN
system and also with the sensors the irrigation system in
agriculture has been improved. The sensors are mostly
used for the measurement of soil matrix and volumetric
level of the soil. Then updation has been made to sensors
and with the combination of WSN system increased to
help the irrigation system of agriculture.
The Introduction of Wireless Sensor Network (WSN) and
Wireless Sensor and Actuation Network (WSAN)
technologies has been used along with the sensors to
sense the water levels and to know about soil content
level.
The Usage of updated sensors in environmental
monitoring have improved the management of fresh water
level usage and because of WSAN based System in
agriculture monitoring made this simple and cheapest one.
The sensing technology with WSN systems gives changes
in the agriculture monitoring.
4. PROPOSED SYSTEM DESIGN
The block representation of the agricultural intelligent
management is represented fig1. In this system, raspberry
pi acts as the master source. The combination of
temperature, humidity, pH and soil moisture sensors are
together added to input pins. Water, monitor, spray
fertilizer and web server are coupled together as output
pins.

Fig -1: Block Diagram
4.1 Components Description
Raspberry pi
Raspberry is the miniature size of the computer which
can perform all operations which can be done in a
computer. It consists of 40 GPIO pins for input and output
connections. In ARM CPU/GPU where CPU for accepting
the input, performing calculations and producing output
and GPU for dealing with graphics output. HDMI acts as a
connector using a cable. RCA jack for analog TV connection
and other output devices. USB port for connecting mouse
and keyboard. 5 volt micro USB power connector can be
plugged for power supply. An SD card slot with operating
system is needed for booting the device. Audio output is an
3.55 mm jack for audio output such as headphones or
speakers and there is no audio in here. LED is present for
the light indicating purpose. The raspberry pi used in this
proposed system is shown in fig2.
Fig 2: Raspberry pi
Temperature Sensor
LM35 is an integrated circuit sensor which is used to
identify the temperature. Here the obtained voltage is
proportional to centigrade. This sensor works at an range
of -55˚ to +120˚C. It has an capability of heating itself and
operates from 4 to 30 volts. This sensor can be easily
devoted for remote applications. It consists of three pins
the first pin for supply voltage second pin for output
voltage and the last pin for ground connection. Fig3 is the
representation of the LM35 temperature sensor for the
purpose of sensing the temperature present in the
agricultural land.
Fig 3: LM35 Temperature sensor
Humidity Sensor
This sensor is used to report the relative humidity in air. It
is represented in fig3. Moisture and air temperature can
be easily sensed by means of it. The sensor has an non
conductive polymer film in midst of two metal plates. The
moisture present in the air is collected through that film
which may cause voltage between those plates. The level
of moisture content in air is found as digital readings from
that voltage changes.And thus how the humidity can be
sensed easily.

Fig 3 Humidity: sensor
PH Sensor
PH sensor is used to detect the acidity and alkalinity level
in the water. It tells whether the water content is acid,
base or neutral.
Fig 3: PH sensor
Soil Moisture Sensor
The moisture sensor is used to identify the water content
in the field. It gives continuous and automatic
measurements of the soil. As shown in fig3. there will be
two rods present in it which will be inserted directly into
the soil for the measurements to produce differential
analog DC voltage output. It can be calibrated to different
types of soil under different types of conditions. The
reading lies between ±3 % of the original moisture
content and also measures the changes of about 0.1 %.
This sensor is not capable of affected by fertilizers or soil
and it is resistant from shock and power. It has an
capability of not affected by soil or fertilizer present in the
field.
Fig 3: Soil Moisture sensor
4.2 Working Description
The sensor network gathers the data from the various
sensors and then transmits it to the main server using
raspberry pi and those parameters can be monitored using
web server. This system monitors the quantity of pH,
humidity, temperature and soil moisture. A particular
range of pre-defined values is set for soil moisture and
temperature, so that it can be changed according with the
type of soil. If the temperature and moisture of the soil
varies from those pre-defined values then the watering
system will be turned on. If the soil temperature is high or
if the soil is dry then the proposed process started to work
by turning on/off. Similar way by knowing the fertilizer
content in soil the fertilizer sprayer can be used for sowing
the fertilizer into the field.
In case if the sensors get departed from predefined values
then the master source gives information to the web
server and turns on the pump and also started to spray the
fertilizer at the time of requirement for soil as per the
values set earlier without any human interaction in the
field.
In case of emergency long distance monitoring can be
done to owner of the field through web server. Through
this one can sense and measure easily in an automated
manner. So that problem faced by farmers like over
logging of water in the field, amount of fertilizer needed
can be identified and overcome easily. And thus IoT plays
an vital role in the intelligent management of agricultural
fields .
5. RESULT AND CONCLUSION
The paper was proposed to provide agricultural process in
an user friendly manner by transmitting the information
about the field condition to the owner of land. And also the
wastage of water can be controlled by ON/OFF and
spraying of fertilizer is done automatically when it is
essential to the field. Thus the smart agriculture is
achieved through this intelligent management system. The
use of this proposed work finds application in water area,

land and farm field. In addition the usage of resource in
land is limited along with less usage of energy and the
labor cost.
6. FUTURE WORK
Each crop may require different variety of fertilizers but in
this system the respective fertilizer needed for each and
every variety of crops cannot be analyzed easily. So thus
by analyzing the type of fertilizer and the amount of
fertilizer needed for each type of plant it can be used in the
field directly from the fertilizer itself by the information
displayed on the web server. So this is the prospect of the
future work.
REFERENCES
[1] F.Viani, M.Bertolli, M.Salucci and A. Polo “Low-Cost
Wireless Monitoring and Decision Support for Water
Saving in Agriculture,” IEEE Sensor Journal, vol,0,Mar.
2017
[2] Mengzhen Kang, and Fei-Yue Wang , “From Parallel
Plants to Smart Plants: Intelligent Control and
Management for Plant Growth” IEEE/CAA Journal of
Automatica , vol.4, no.2, April 2012.
[3] Wonjong Song, Student Member, IEEE, Junan Lee,
Nayeon Cho, and Jinwook Burm, Member, IEEE “An
Ultralow Power Time-Domain Temperature Sensor
With Time-Domain Delta–Sigma TDC,” IEEE
Transaction on Circuits and Systems—II: Briefs, vol.
64, no.10, October 2017.
[4] Alex Martinez-Agirre, Jesus Álvarez-Mozos, Hans
Livens, and Niko E.C.Verhoest ”Influence of Surface
Roughness Measurement “ IEEE transactions on
geoscience and remote sensing, vol. 55,no.10, october
2017.
[5] Francisco Yandun, Giulio Reina, Miguel Torres-
Torriti, George Kantor, and Fernando Auat Cheein, “A
Survey of Ranging and Imaging Techniques for
Precision Agriculture Phenotyping” IEEE/ASME
Transactions on Mechatronics, 2017.
[6] D.Herrera, S.Tosetti and R.Carelli, Senior Member,
IEEE “Dynamic Modeling and Identification of an
Agriculture Autonomous Vehicle” IEEE Latin America
transactions,vol.14,no.6,june2016.
[7] Pratap Tokekar, Joshua Vander Hook, David Mulla,
and Volkan Isler, “Sensor Planning for a Symbiotic
UAV and UGV System for Precision Agriculture” IEEE
Transactions on Robotics , 2016.
[8] Ahmad Ashraf Abdul Halim, Najmuddin Mohd Hassan,
Ammar Zakaria ,Latifah Munirah Kamaruddin , Asyraf
Hakimi Abu Bakar , “Automated Scheduling Based on
Plant Growth For Greenhouse Management System “
2016 3rd International Conference on Electronic
Design (ICED), August 11-12, 2016, Phuket, Thailand
[9] Abdul Motin Howlader, Student Member, IEEE,
Naomitsu Urasaki, Member, IEEE, and Ahmed Yousuf
Saber, Senior Member, IEEE” IEEE transactions on
industry applications,vol.50,no.5, September/October
2014
[10] Joaquin Gutierrez, Juan Francisco Villa-Medina,
Alejandra Nieto-Garibay, and Miguel Angel Porta-
Gandara “Automated Irrigation System Using a
Wireless Sensor Network and GPRS Module ” IEEE
transactions on instrumentation and measurement,
2013.
[11] Nicolas Baghdadi, Remi Cresson, Eric Pottier, Fellow,
IEEE, Maelle Aubert, Mehrez Zribi, Andres Jacome, and
Sihem Benabdallah “A Potential Use for the C-Band
Polarimetric SAR Parameters to Characterize the Soil
Surface “ IEEE transactions on geosciences and
remote sensing,vol.50,no.10,october2012.
[12] Suhinthan Maheswararajah, Saman K. Halgamuge,
Member, IEEE, Kithsiri B. Dassanayake, and David
Chapman ,”Management of Orphaned-Nodes in
Wireless Sensor Networks for Smart Irrigation
Systems” IEEE Transactions on signal processing, vol.
59, no. 10, October 2011.
[13] Paul A. Hammond*, Danish Ali, and David R. S.
Cumming, Member, IEEE ,” A System-on-Chip Digital
pH Meter for Use in a Wireless Diagnostic Capsule”
IEEE transactions on biomedical engineering, vol. 52,
no. 4, April 2005 .
[14] Juan F. Posada, Member, IEEE, Juin J. Liou, Member,
IEEE, and Richard N. Miller, Member, IEEE, “An
Automated Data Acquisition the Characteristics of a
Soil Moisture Sensor System for Modeling” IEEE
transactions on instrumentation and measurements ,
vol.40, no. 5, October 1991.

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