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IOT based smart security and monitoring devices for agriculture

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Agriculture sector being the backbone of the Indian economy deserves security. Security not in terms of resources only but also agricultural products needs security and protection at very initial stage, like protection from attacks of rodents or insects, in fields or grain stores. Such challenges should also be taken into consideration. Security systems which are being used now a days are not smart enough to provide real time notification after sensing the problem. The integration of traditional methodology with latest technology as Internet of Things can lead to agricultural modernization. Keeping this scenario in mind an ‘Internet of Things’ based device have been designed, tested and analyzed which is capable of analyzing the sensed information and then transmitting it to the user. This device can be controlled and monitored from remote location and it can be implemented in agricultural fields, grain stores and cold stores for security purpose. This paper is oriented to accentuate the methods to solve such problems like identification of rodents, threats to crops and delivering real time notification based on information analysis and processing without human intervention. In this device, mentioned sensors and electronic devices are integrated using Python scripts. Based on attempted test cases, we were able to achieve success in 84.8% test cases.

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IOT based smart security and monitoring devices for agriculture

  1. 1. DEVELOPMENT OF IOT BASED SMART SECURITY AND MONITORING DEVICES FOR AGRICULTURE SNEHA DAISE PAULSON EC-2, S7 ROLL NO-40
  2. 2. TOPICS DISCUSSED HERE: Brief understanding of IOT Implementing these in agricultural sector  The design, test and analysis of the device
  3. 3. term was proposed by Kevin Ashton in 1999 Connection of each and every thing to the internet Relationship will be people-people, people-things & things-things WHAT IS INTERNET OF THINGS ? Kevin AshtonRFID
  4. 4. IOT Make things “smart” (UID) Far away communication with smart devices Automate things (ultimate goal)
  5. 5. Nest Learning Thermostat HAIKU CEILING FAN JUNE INTELLIGENT OVEN
  6. 6. 20-30% loss of agricultural products. 5-10% loss in rice crops, in Asia due to rodents. Death rate due to rodent borne diseases is higher in comparison with illness like HIV-AIDS. WHY IOT IS IMPLEMENTED IN AN AGRICULTURAL SECTOR?
  7. 7. USE SMART SECURITY SYSTEMS USING IOT.
  8. 8. Remember, IOT can be implemented in various other agricultural fields like: • water quality monitoring • monitor soil constituent, soil humidity • water irrigation • pest monitoring
  9. 9. Raspberry Pi 2 Model B+ PIR sensor Ultrasonic Ranging Device Web camera Ultrasonic Sound Repeller COMPONENTS USED :
  10. 10. PIR sensor URD Ultrasonic repeller
  11. 11. PTC’s ThingWorx’s IOT platform Python Linux based Raspbian OS Platform and language used:
  12. 12. Block diagram
  13. 13. Circuit Explanation Sensors are connected to GPIO header. PIR sensors have three pins: Vcc, OUT, GND URD (HC-SR04) has 4 pins: TRIG, ECHO, Vcc, GND The repeller will be activated by server based upon data analysis.
  14. 14. Raspberry pi B+ GPIO header consists of 40 pins. HC-SR04-ECHO port is rated as 5V. The GPIO input pin is rated as 3.3V. So we use a voltage divider circuit (1KΩ and 2KΩ.) In addition, web camera is connected to universal serial bus port of raspberry pi.
  15. 15. Screenshot of the prototype
  16. 16. Circuit installation Set up in a small area of size 10sq.m. Installed in the corner with sensors facing same side and cameras fixed at some height. Then the test can be started.
  17. 17. Python scripts will identify the motion of rodents. URD sensor will be activated and simultaneously webcam is activated to capture a snap of area. The analyzed data and information is stored in SQL based database. Repeller will be activated at a frequency (30kHz to 65kHz) which is aversive to rodents.
  18. 18. Screenshot of ThingWorx’s Platform Test
  19. 19. A success rate of 84.8% i.e. 118 test cases out of 139 test cases. Unsuccessful test cases i.e. 15.2% were due to devices connectivity, data transmission, notification, and other factors. Result analysis
  20. 20. location of device in area can also be changed based upon the location of grains. Future scope and conclusion A grid of sensor panels consisting PIR sensors and URD sensors can be added. Can incorporate pattern recognition techniques.
  21. 21. References • www.cisco.com/c/en_in/solutions/internet-of things/overview.html • https://en.wikipedia.org/wiki/Internet_of_things • http://www.postscapes.com/internet-of-things-definition/ • https://www.internetsociety.org/sites/default/files/ISOC-IoT-Overview- 20151014_0.pdf • https://www.itu.int/en/action/broadband/Documents/Harnessing-IoT- Global-Development.pdf • http://mpi-group.com/wp-content/uploads/2016/01/IoT- Summary2016.pdf • http://www.ijarcce.com/upload/2016/march-16/IJARCCE%20226.pdf • http://dupress.deloitte.com/content/dam/dup-us-en/articles/iot-primer- iot-technologies- applications/DUP_1102_InsideTheInternetOfThings.pdf • https://en.wikipedia.org/wiki/Python_(programming_language)
  22. 22. • http://www.tutorialspoint.com/python/python_tutorial.pdf • http://www.ptc.com/internet-of-things/solutions#sthash.jBBZc • http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=7387818

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