Uploaded by editing

THE ROLE OF IOT IN AGRICULTURE FIELDS

advertisement
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 01, January 2019, pp. 858–866, Article ID: IJMET_10_01_089
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=01
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
THE ROLE OF IOT IN AGRICULTURE FIELDS
Dr. BALAJEE MARAM
Sr. Asst. Prof., Dept. of CSE, GMR Institute of Technology, Rajam, INDIA.
Dr. Guru KesavaDasu Gopisetty
Professor & HOD, Dept. of CSE, Eluru College of Engineering &Technology, Eluru
ABSTRACT
Today, majority of the farmers are dependent on agriculture for their survival. But
majority of the agricultural tools and practices are outdated and it yields less crop
products, because everything is depends on environment and Government support. The
world population is becoming more comparatively cultivation land and crop yield. It is
essential for the world to increase the yielding of the crop by adopting information
technology and communication plays a vital role in smart farming. The objective of this
research paper to present tools and best practices for understanding the role of
information and communication technologies in agriculture sector, motivate and make
the illiterate farmers to understand the best insights given by the big data analytics using
machine learning.
Keywords: Agriculture, IoT, Sensors, Smart Farming, Raspberri Pi3
Cite this Article: Dr. Balajee Maram and Dr. Guru KesavaDasu Gopisetty, the Role of Iot
in Agriculture Fields, International Journal of Mechanical Engineering and Technology,
10(01), 2019, pp.858–866
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&Type=01
1. INTRODUCTION
Most of the rural residents are depends on agriculture. Still the traditional tools and techniques
have been using for agriculture in the world. Today the world is equipped with latest technology
in every field including the agriculture sector. Artificial Intelligence, robots, sensors and machine
learning gives more knowledge in various sectors. Today the world is monitored by various types
of machines including sensors. Very large amount of data is being generated from various data
sources like satellites, sensors etc. The land for cultivation is being decreased, because of
urbanization. There is a need to increase crop product drastically. For achieving high yield from
agriculture, there is a need to adopt latest information technology and communication
technologies. Big data is became popular for analyzing the agricultural big data. Management of
agriculture and crop yield can be analyzed by big data analytics (Kamilaris et al., 2016).
The Agricultural Big Data collects and stores the information about farmers, crops, pest
control for different crops, climate change in specific areas, water availability in different places
and seasons, Government support for different crops etc. The entire Agricultural Big Data stored
in various storage devices which is under the control of 3rd party vendors. Here data privacy
http://www.iaeme.com/IJMET/index.asp
858
[email protected]
Dr. Balajee Maram and Dr. Guru KesavaDasu Gopisetty
(Nandyala and Kim, 2016) and security come into picture. Because the Agricultural Big Data is
a combination of public data, private data, industrial data and data from Government. So data
privacy (Carolan, 2016; Schuster, 2017) and security is required for private data and industrial
data.
2. EXISTING TECHNOLOGY
2.1. Smart Agriculture
Modern agriculture involves usage of large number of agricultural implements from sowing to
crop harvesting and storages. Using a large number of sensors makes the entire operation smart
and cost effective due to high precision. The agriculture sector has been experiencing automation
and robotic equipment are playing crucial operational precision. Autonomous tractors, robotic
harvesters, robotic weeders and robotic data scouts currently complement farm equipment. Aerial
imagery is another operational technique where large farm areas are monitored. Satellites, planes
and recently unmanned drones are deployed for areal imagery operations. For achieving the smart
agriculture, the following tools and technologies are required:
• Sensor fusion
• Combinational sensors
• Developing newer sensor types
• Robotics
• Remote Sensing
• In-field Sensing
• Data Management
• Variable Rate Applications
• Telematics
• In-field Sensing
• Artificial intelligence (AI)
• Key Sensors, Robotics and Artificial Intelligence
2.2. Need of Sensors in agriculture
A significant lose 10% of global food production, can happen with plant diseases and pest in
commercial agricultural crops in the world. Pesticides are very helpful to control the plant
diseases and pests. The usage of pesticides can increase the cost for yielding the commercial
crops and raises the danger of toxic residue. Once the plant diseases and pests are identified in
advanced, then Plant diseases and pest control would be efficient. The cost for identifying these
diseases and pests using traditional approaches is more. Today, the new technology came into the
public. The entire agriculture fields are covered by satellites, sensors, drones etc. The farmers are
using drones for collecting and maintaining the data of different crops, pests, water facilities etc.
The key reasons for using sensors are:
• Absolute need to increase global food production
• Being able to analyze soil conditions
• Adverse environmental impact reduction
• Continuous monitoring to enable corrective measures in real time
• High precision achieved while using agricultural implements
• Effective cost savings by waste reduction
http://www.iaeme.com/IJMET/index.asp
859
[email protected]
The Role of Iot in Agriculture Fields
• Automate agricultural produce monitoring
The remote sensing system provides a harmless and cost-effective techniques for identifying
and quantifying the plant diseases and pests easily. With rapid urbanization, cultivable area is
likely to shrink. In order to arrest decline and increase crop production both agricultural scientists
and sensor technologists have been working closely. Types of sensors for agriculture sector
• Biological
• Water sensors
• Meteorological sensors
• Range finders
• Temperature sensors
• Sensors for positioning, navigation, and obstacle detection
• Level sensors – for crop storages silos
• LiDAR, Radar, Laser, Sonar – for accident avoidance
• Hygrometers
• Position sensors – sprayers, harvesters, sowing
• GPS (for geolocation-GNSS receivers, RTK signals) - can accurately position a
moving vehicle within 30cm or less using GPS
3. PROPOSED SYSTEM
In this paper the updated data regarding temperature and humidity in any areas can be accessed
using sensor, Raspberry pi3 and IOT technology. The main aim of the proposed system is to
monitoring the real time temperature and relative humidity in a cost effective at fixed interval of
time. In this monitoring node is raspberry pi3 and sensor is DHT11.Raspberry pi is used to store
and display the temperature and relative humidity, and is programmed using python language.
The DHT11 Temperature & Humidity Sensor features a temperature & humidity sensor provides
digital output but DHT11 is mainly used for the humidity measurement. Features of sensors
includes high reliability and long term stability In this the temperature is displayed degree Celsius
or Fahrenheit. The data being processed by Raspberry pi will be updated continuously on cloud
server. WIFI is attached so that it fetches data with help of internet and user can access or control
temperature and humidity from any part of the world.
Advantages of proposed system
We can get present information regarding temperature and humidity in a particular place.
The size of python code is very less and it reduces the time complexity and space complexity
in raspberry pi 3 model B.
It is very useful for industries, pharmacies as it is used to detect the temperature.
Accessible from anywhere, efficient, user friendly.
Block diagram
The real-time temperature and humidity is being monitored by the proposed system
effectively. Here monitoring node is raspberry pi3 model b and sensing node is DHT11
Temperature and Humidity sensor. The sensor is connected to raspberry pi3 model b with the
help of jumper wires. The jumper wires must connect to GPIO pins of the raspberry pi3 model b.
Raspberry pi3 module is programmed using python language. The output temperature is
displayed in terms of degree Celsius or Fahrenheit or both as required. In this project, Sqlite
queries is used to retrieve data from the database. By creating the tables data is stored into those
tables along with date and time. Minimum, Maximum and Average values of the temperature and
http://www.iaeme.com/IJMET/index.asp
860
[email protected]
Dr. Balajee Maram and Dr. Guru KesavaDasu Gopisetty
humidity over a certain period also known with the help of Sqlite. The sensing data is
continuously entered into database. Sqlite queries is used to retrieve those data. The xming server
is used to interact with raspberry pi 3 model b module. The block diagram of the proposed system
is displayed below:
Figure 1: Block diagram of Raspberry Pi3 Model B
Sequence diagram
The Sequence Diagram models the collaboration of objects based on a time sequence. It
shows how the object “user” interacts with others “Raspberri Pi” and “database”.
Figure 2: Sequence Diagram
4. IMPLEMENTATION
The data is being gathered from various data sources in the world using sensors, satellites,
machines etc. Here IoT plays an important role. In this paper, a simple IoT implementation has
been initiated for gathering temperature data and humidity data from various sensors. Then the
data is analyzed and give predictions to the society like what type crop is suggestable based on
the current climate, based on water level, based on price supported by Government etc.
Process to connect raspberry pi to laptop
• Firstly make wifi connection into shared mode.
• Connect the raspberry pi to the laptop using USB cable and ethernet cable.
• Then advanced IP scanner is used to scan the IP address of raspberry pi.
• After that putty and Xming server is used to view the GUI interface.
Xming server
By using this GUI interface is obtained. It is act like a server for windows. It is fast, simple
to install.
Advanced IP Scanner
http://www.iaeme.com/IJMET/index.asp
861
[email protected]
The Role of Iot in Agriculture Fields
It scans all the IP addresses in a range of IP addresses and their ports without installing the
software.
Putty
It is a free and open-source terminal emulator, serial console and network file transfer
application.
Figure 3: Putty
Enter IP address in the host name field.
Now open SSH and in that double click X11 , then enable X11 Forwarding.
Secure Socket Shell (SSH) provides administrators to access a remote computer securely.
X11 forwarding can be useful when a GUI is required, especially for system and configuration
tools that don't have a CLI interface.
Figure 4: PuTTY Configuration
After giving IP address in putty, a terminal will be displayed where we have to give login ID
and password. By default login ID is pi and password is raspberry but we can change the password
using passwd command.
http://www.iaeme.com/IJMET/index.asp
862
[email protected]
Dr. Balajee Maram and Dr. Guru KesavaDasu Gopisetty
Now the commands lxterminal and startlxde are used to view the GUI interface
Connections
Now connect Raspberry pi with dht11 sensor using bread board and jumper wires
RaspberryPi
DHT11Module
3.3vP1 ————————VCC (V)
GNDP6 ———————— GND (G)
GPIO17 P11 ——————DATA (S)
Figure 5: Connections
5. RESULTS AND DISCUSSION
The data has been taken from the sensors using IoT setup. The data is as follows:
http://www.iaeme.com/IJMET/index.asp
863
[email protected]
The Role of Iot in Agriculture Fields
Table 1: Temperature and Humidity from IoT devices
Date
29.03.2018
31.03.2018
03.04.2018
Sample 1
Temp:
30
Sample2
Temp:
31
Sample 3
Temp:
30
Max
Temp:
31
Min
Temp:
30
Avg
Temp:
30.33
Humidity:
Humidity:
Humidity:
Humidity:
Humidity:
Humidity:
77
Temp:
29
78
Temp:
28
77
Temp:
30
78
Temp:
30
77
Temp:
28
77.05
Temp:
29
Humidity:
Humidity:
Humidity:
Humidity:
Humidity:
Humidity:
75
Temp:
31
76
Temp:
32
77
Temp:
30
77
Temp:
32
75
Temp:
30
76
Temp:
31
Humidity:
Humidity:
Humidity:
Humidity:
Humidity:
Humidity:
74
72
70
74
70
72
From the above table, we take three samples of temperature and humidity. The sample are
from three different days. Temperature and humidity are changes day to day in surrounding
environment. From these three different samples calculate minimum, maximum and average
values of both temperature and humidity.
6. CONCLUSION
This paper presents different tools and techniques for implementing smart farming. The
output has various insights including weather forecast, pest control, suitable crops for the
soil, water requirements analysis, crop price supported by Government, dose of fertilizers
for various crops, crop yields etc. The analysis also intimate the farmers about
Government current schemes, monsoon notifications, subsidies from Government and
private sector, latest news about various types of pests etc. The implementation in this
paper gives the results about the temperature and humidity in the agricultural fields using
sensors. IoT-Based temperature and humidity monitoring system provide an efficient and
reliable system for monitoring. The proposed methodology is able to interface the
Raspberry pi. Consequently, by using DHT11 sensor we are able get the temperature and
humidity values. And then these values are stored in the database with corresponding date.
Therefore, the minimum, maximum, average values are retrieved from the database for
the specified date.The future work will be focusing on big data where all types of data is
collected from various data sources using IoT in the world, store the data in cloud, analyze
the cloud data and apply machine learning methods for getting the desired outcome in
agriculture fields.
http://www.iaeme.com/IJMET/index.asp
864
[email protected]
Dr. Balajee Maram and Dr. Guru KesavaDasu Gopisetty
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
Andreas Kamilaris, Andreas Kartakoullis, Francesc X. Prenafeta-Boldú, A review on the
practice of big data analysis in agriculture, Computers and Electronics in Agriculture 143
(2017) 23–37.
Karmas, A., Karantzalos, K., Athanasiou, S., 2014. Online analysis of remote sensing data
for agricultural applications. s.l., OSGeo’s European conference on free and open
source software for geospatial.
Karmas, A., Tzotsos, A. &Karantzalos, K., 2016. Geospatial Big Data for Environmental
and Agricultural Applications. In: s.l.: Springer International Publishing, pp.353-390
Kim, G.H., Trimi, S., Chung, J.-H., 2014. Big-data applications in the government sector.
Commun. ACM 57 (3), 78–85.
Nandyala, C.S., Kim, H.-K., 2016. Big and meta data management for U-agriculture mo
bile services. Int. J. Software Eng. Appl. (IJSEIA) 10 (1), 257–270.
Schuster, J., 2017. Big data ethics and the digital age of agriculture. Am. Soc. Agric. Biol.Eng.
24 (1), 20–21.
Tripathi, S., Srinivas, V.V., Nanjundiah, R.S., 2006. Downscaling of precipitation for climate
change scenarios: a support vector machine approach. J. Hydrol. 330 (3), 621–640.
Salvador García*, Sergio Ramírez-Gallego, Julián Luengo, José Manuel Benítez and
Francisco Herrera, Big data preprocessing: methods andprospects, García et al. Big Data
Analytics (2016) 1:9, DOI 10.1186/s41044-016-0014-0
K. Ravisankar, K. Sidhardha, Prabadevi B, Analysis of Agricultural Data Using Big Data
Analytics,Journal of Chemical and Pharmaceutical Sciences, July - September 2017, Volume
10 Issue 3, ISSN: 0974-2115, pp. 1132-1135
S.Balamurugan, N.Divyabharathi, K.Jayashruthi, M.Bowiya, R.P.Shermy and
Dr.R.GokulKrubaShanker, Internet of Agriculture: Applying IoT toImprove Food and
Farming Technology, International Research Journal of Engineering and Technology
(IRJET), Volume: 03 Issue: 10 | Oct -2016, e-ISSN: 2395 -0056, p-ISSN: 2395-0072, pp.713719.
J.InfantialRubala, D. Anitha, Agriculture Field Monitoring using Wireless Sensor Networks
to Improving Crop Production, International Journal of Engineering Science and Computing,
March 2017, pp. 5216-5221.
ShikhaUjjainia, PratimaGautam, S.Veenadhari, Development of Smart Crop Production
System usingBig Data- A Review, International Journal of Research and Innovation in
Applied Science (IJRIAS)|Volume II, Issue V, May 2017|ISSN 2454-6194, pp.19-21.
Haoran Zhang, Xuyang Wei, Tengfei Zou, Zhongliang Li, and Guocai Yang, Agriculture Big
Data: Research Status, Challenges and Countermeasures, International Federation for
Information Processing 2015, D. Li and Y. Chen (Eds.): CCTA 2014, IFIP AICT 452, pp.
137–143, 2015. DOI: 10.1007/978-3-319-19620-6_17
Keith H. Coble, Ashok K. Mishra, Shannon Ferrell, and Terry Griffin, Big Data in
Agriculture: A Challenge for the Future, Applied Economic Perspectives and Policy (2018)
volume 40, number 1, pp. 79–96. doi:10.1093/aepp/ppx056.
S.VinilaKumari, Dr. P Bargavi, U. Subhashini, Role of Big Data Analytics in Agriculture,
Special Issue on Computational Science, Mathematics and Biology IJCSME-SCSMB-16March-2016 ISSN-2349-8439, pp.110-113.
SjaakWolfert,LanG, CorVerdouw, Marc-JeroenBogaardt, Big Data in Smart Farming – A
review, Agricultural systems, 153 (2017), pp.69-80.
Bharath G, Anala M R, Big Data Analytics in Precision Agriculture: A Survey, International
Journal of Research and Scientific Innovation (IJRSI) | Volume IV, Issue VIS, June 2017 |
ISSN 2321–2705, pp.162-166
http://www.iaeme.com/IJMET/index.asp
865
[email protected]
The Role of Iot in Agriculture Fields
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
Meetali V. Rasal, Prof. Jaideep G. Rana, Raspberry Pi Based Weather Monitoring System,
International Journal of Advanced Research (IJAR) in Computer and Communication
Engineering-Vol. 5-October 2016`.
BalajeeMaram K. and J.M. Gnanasekar, UNICODE Text Security Using Dynamic and KeyDependent 16X16 S-Box, Australian Journal of Basic and Applied Sciences, 10(1) January
2016, Pages: 26-36.
Piotr Mroczkowski, Generating Pseudorandom S-Boxes – a Method of Improving the
Security of Cryptosystems Based on Block Ciphers, Journal of Telecommunications and
Information Technology, 2/2009.
SriramRamanujam and MarimuthuKaruppiah, Designing an algorithm with high Avalanche
Effect, IJCSNS International Journal of Computer Science and Network Security, VOL.11
No.1, January 2011.
Ganesh Patidar, Nitin Agrawal, SitendraTarmakar, A block based Encryption Model to
improve Avalanche Effect for data Security, International Journal of Scientific and Research
Publications, Volume 3, Issue 1, January 2013, ISSN 2250-3153.
BalajeeMaram K, J M Gnanasekar, Light Weight Cryptographic algorithm to Improve
Avalanche Effect for Data Security using Prime Numbers and Bit Level Operations,
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 10, Number
21 (2015) pp 41977-41983.
Parvez khan Pathan, BasantVerma, Hyper Secure Cryptographic Algorithm to Improve
Avalanche Effect for Data Security, International Journal of Computer Technology and
Electronics Engineering (IJCTEE), ISSN 2249-6343, Volume 1 , Issue 2.
Jayant P. Bhoge, Dr. Prashant N. Chatur, Avalanche Effect of AES Algorithm, (IJCSIT)
International Journal of Computer Science and Information Technologies, ISSN 0975-9646,
Vol. 5 (3) , 2014, 3101 – 3103
Ajeet Singh, A New Approach to Enhance Avalanche Effect in AES to Improve Computer
Security, Information Technology & Software Engineering, ISSN: 2165-7866 JITSE, Volume
5, Issue 1.
Adams, C. and S. vTavares, 1990. The structured design of cryptographically good S-boxes,
journal of Cryptology, 3(1): 27-41.
Hussain, I., T. Shah, H. Mahmood and M. Afzal, 2010. Comparative analysis of S-boxes
based on graphical SAC, International Journal of Computer Applications, 2(5): 1-7.
Ahmed, N., Testing an S-Box for Cryptographic Use, International Journal of Computer and
Electrical Engineering, 1-5.
Balajee Maram, J M Gnanasekar, A Block Cipher Algorithm To Enhance The Avalanche
Effect Using Dynamic Key-Dependent S-Box And Genetic Operations, International Journal
of Pure and Applied Mathematics, Volume 119 No. 10 2018, 399-418 ISSN: 1311-8080
(printed version); ISSN: 1314-3395 (on-line version)
Balajee Maram et al. (2018). A framework for Predicting Temperature, Humidity Using
Raspberri pi 3, J. of Advancement in Engineering and Technology, V6I2.04. DOI:
10.5281/zenodo.1219170.
Nivedhitha.G, T. Sujithra, Aksheya Suresh, Bhavadharani, Automation of Parcel Delivery
Collection using IoT – Smart Freight Box. International Journal of Civil Engineering and
Technology, 8(9), 2017, pp. 966–972.
Raghavendran G, Elanchezhiyan R, K. Rajesh, S. Palanivel, Bus Track using IoT and
Prediction Logic, International Journal of Mechanical Engineering and Technology 8(10),
2017, pp. 93–100.
B. Sumathy, G. Gowthaman, K. Hari Haran, G. Keerthee Rajan, A. Sweeto Jeison, Sewage
Level Maintenance using IoT, International Journal of Mechanical Engineering and
Technology 9(2), 2018, pp. 389–397.
http://www.iaeme.com/IJMET/index.asp
866
[email protected]
Download
Random flashcards
Arab people

15 Cards

Pastoralists

20 Cards

Nomads

17 Cards

Create flashcards