International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Towards Autonomous Agents for Precision Agriculture

Abstract

Wireless Sensor Networks are increasingly being applied in Precision Agriculture to minimize farming resources and maximize crop yield. Data on environmental and soil conditions can be collected by Sensor Nodes on a fine scale and transmitted to Base Stations where they are analyzed to aid decision making in a farm. Furthermore, using current Artificial Intelligence techniques, Agents that learn directly from the environment can be deployed such that the entire system, from data collection to analyses and decision making is completely autonomous. This research work presents results for the design and implementation of a low cost Wireless Sensor Network equipped to sense soil moisture, pH and NPK levels as well as environmental temperature and humidity levels. The sensed data is transmitted to a Base Station for online publishing and analyses using a Reinforcement Learning DQN Agent.

Country : Nigeria

1 Udenze Adrian2 Alumona Theophilus Leonard3 Isizoh Anthony Nosike

  1. Department of Computer and Electronics Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Nigeria
  2. Department of Computer and Electronics Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Nigeria
  3. Department of Computer and Electronics Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Nigeria

IRJIET, Volume 7, Issue 11, November 2023 pp. 524-531

doi.org/10.47001/IRJIET/2023.711069

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References

  1. J. V. Stafford, “Implementing precision agriculture in the 21st century”, Journal of agricultural engineering research 76(3), 267-275, 2000.
  2. N Zhang, M Wang and N Wang, “Precision agriculture – a worldwide overview”, Computers and electronics in agriculture, Elsevier, 2002.
  3. D. K. Shannon, D. E. Clay and N. R. Kitchen, “Precision Agriculture Basics, American Society of Agronomy”, Print ISBN:9780891183662 |Online ISBN:9780891183679, 2018.
  4. A Udenze, “Power Management Algorithms for Wireless Sensor Networks”, Lambert Academic Publishing, Germany, 2016.
  5. M. Zhang, M. Li, W. Wang, C. Liu and H. Gao, “Temporal and spatial variability of soil moisture based on WSN”. Mathematical and Computer Modeling, Elsevier Ltd., 2012.
  6. D. Thakur, Y. Kumar, A. Kumar and P. K. Singh’ “Applicability of Wireless Sensor Networks in Precision Agriculture: A Review”, Wireless Personal Communication, Springer Science and Business Media, 2019.
  7. R. K. Kodali, N. Rawat and L. Boppana, “WSN sensors for precision agriculture”, IEEE region 10 Symposium, 2014.
  8. K. Jha, A. Doshi P. Patel, M. Shah 2019, “A comprehensive review on automation in agriculture using artificial intelligence”, Artificial Intelligence in Agriculture, 2019.
  9. D. Shadrin, A. Menshchikov, A. Somov and G. Bornemann, “Enabling precision agriculture through embedded sensing with artificial intelligence”, IEEE Transactions on Instrumentation and Measurement, pp(99):1-1, 2019.
  10. A.G. Mohapatra, S. K. Lenka and B. Keswani, “Neural network and fuzzy logic based smart DSS model for irrigation and control in precision agriculture”, Proceedings of the national academy of sciences, India Section A: Physical Sciences 89 (1), 67-76, 2019.
  11. A.Chlingaryan, S. Sukkarieh, B. Whelan, “Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review”, Computers and Electronics in agriculture 151, 61-69, 2018.
  12. Y. Mekonnen, S. Namuduri, L. Burton A. Sarwat, and S. Bhansali, “Machine learning techniques in wireless sensor network based precision agriculture”, Journal of Electrochemical society 167 (3) 037522, 2019.
  13. M. Johnson, M. Healy, P. van de Vin, M. Hayes, J. Nelson, T. Newe and E. Lewis, “A comparative review of wireless sensor node technologies”, IEEE sensors 2009 conference, 2009.
  14. Arduino, https://www.arduino.cc/, 2021.
  15. Raspberry Pi, https://www.raspberrypi.org/, 2021.
  16. Zigbee, http://www.zigbee.org/Specifications/ZigBee/FAQ.aspx, 2021.
  17. S. J. Russell and  P.Norvig, “Artificial Intelligence: A modern approach”, Prentice Hall, 2010.
  18. R. Sutton and A Barto, “Reinforcement Learning: An introduction”, MIT Press, London, 2014.
  19. Deep Neural Networks, https://www.tensorflow.org, 2021.
  20. x-raying the Nigerian tomato industry, https://www.pwc.com, 2021.
  21. E. A. Onwubuya, E. O. Okporie and M. G. Nenna, “ Nsukka yellow pepper processing and preservation techniques among women farmers in Enugu State”, African journal of agricultural research, Vol.4(9), pp. 859-863 , 2009.
  22. N. E. Abu and C. V. Odo, “The effect of plant density on growth and yield of  ‘NsukkaYellow’ aromatic pepper (Capsium annum L.)”, African journal of agricultural research, vol. 12(15), pp. 1269-1277, 2017.
  23. Anchor borrowers programme, https://www.cbn.gov.ng, 2021.
  24. S. E. Obalum, I. G. Edeh, O. N. Imoh, O. M. Njoku, I. M. Uzoh, V. N. Onyia, C. A. Igwe and J. M. Reichert, “Agronomic evaluation of seedbed and mulching alternatives with plant spacing for dry-season fluted pumpkin in coarse-textured tropical soil”, Food and Energy Security, Vol. 6, Issue 3, p. 113-122, Wiley Inline Library, 2017.
  25. Nwite, J. C., E. N. Ogbodo, S. E. Obalum, V. C. Igbo, and C. A. Igwe. 2012. Short-term response of soil physical properties of an Ultisol, and nutrient composition of fluted pumpkin to organic and inorganic fertilizer mixtures. J. Biol. Agric. Healthcare 2:195–204.
  26. I.Bhakta, S. Phadikar and K. Majumder, “State-of-the-art technologies in precision agriculture: a systematic review”, Society of Chemical Industry. Online Wiley Library, DOI 10.1002/jsfa.9693, 2019.
  27. Thingspeak, https://www.thingspeak.com, 2021.
  28. R. K.  Math and N. V. Dharwadkar, “A Wireless Sensor Network Based Low Cost and Energy Efficient Frame Work for Precision Agriculture”, 2017 International Conference on Nascent Technologies in the Engineering Field (ICNTE-2017), 2017.
  29. S. Adebayo 2015, A.O. Akinwunmi, H.O. Aworind and E.O. Ogunti. “Increasing Agricultural Productivity in Nigeria Using Wireless Sensor Network (WSN)”, African journal of computing and ICT. Vol 8, no 3, issue 2, 2015.
  30. Generic soil hygrometer module, components101.com, 2021.
  31. DHT22 Data-sheet, https://www.alldatasheet.com/, 2021.
  32. NPK Sensor, https://www.jxctiot.com, 2021.
  33. SEN0161 Datasheet and application note, https://www.application-datasheet.com/pdf/dfrobot/509083/sen0161.html, 2021.
  34. S. Ferdoush and X. Li, “Wireless Sensor Network System Design Using Raspberry Pi and Arduino for Environmental Monitoring Applications”, Procedia Computer Science, Elsevier. Vol 34, 103-110, 2015.
  35. Python, https:/www.python.org, 2021.
  36. Gym AI, https://www.gym.openai.com, 2021.
  37. A.Udenze, “Application of data mining techniques to problems in fund raising”, International journal of current research and review, vol. 6, issue 22, pp. 1-5, 2014.
  38. Patricio and R. Rieder 2018, “Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review”, Elsevier Computers and Electronics in Agriculture Volume 153, October 2018, Pages 69-81, 2018.
  39. Sim800 Data-sheet, https://datasheetspdf.com/pdf/989664/SIMCom/SIM800L/1sw, 2021.
  40. T. Talaviya, D. Shah, N. Patel, H. Yagnik and M. Shah, “Implementation of artificial intelligence in agriculture for optimization of irrigation and application of pesticides and herbicides”, Artificial Intelligence in Agriculture 4, 58-73, 2020.
  41. N.S. Rao, S. K. Soam, C. S. Rao “Application of artificial intelligence in precision agriculture”, gradivareview.com, 2021.
  42. G. Bannerjee, U. Sarkar, S. Das I. Ghosh. “Artificial intelligence in agriculture: A literature review”, International journal of scientific research in computer science applications and management studies 7(3), 1-6, 2018.
  43. K. Gurney, “An introduction to neural networks”, CRC press, 2018.
  44. A.Udenze and K. McDonald-Maier, “Dyna-Routing: Multi Criteria Reinforcement Learning Routing for Wireless Sensor Networks with Lossy Links”, Ad-Hoc and Sensor Wireless Networks, vol. 11, issue 3, pp. 285-306, 2011.
  45. A.Udenze, “HYMAC: an intelligent collision avoiding dynamic MAC for Wireless sensor networks”, International journal of engineering research & technology, vol. 3, issue 11, pp. 380-386, 2014.
  46. Y. Mekonnen, S. Namuduri, L. Burton A. Sarwat, and S. Bhansali, “Machine learning techniques in wireless sensor network based precision agriculture”, Journal of Electrochemical society 167 (3) 037522, 2019.
  47. V. Vijayakumar and N. Balakrishnan, “Artificial intelligence based agriculture automated monitoring system using WSN”. Journal of Ambient Intelligence and humanized computing, 1-8, 2021.
  48. J. E. Motes, J. T. Criswell and J. P. Damicon, “Pepper production. Oklahoma cooperation extension fact sheets. https://osfacts.okstate.edu. 2021.
  49. Agric4profits, https:/agric4profits.com/ugu, 2021.
  50. L. Espinoza, N. Slaton and M. Mozaffari, “Understanding the numbers on your soil test report”, University of Arkansas System, Division of Agriculture Research and Extension, https://www.uaex.edu, 2021.
  51. Veggiegrow, https://www.veggiegrow.ng, 2021.
  52. LDR module, https://www.sunrom.com/p/light-sensing-module-ldr, 2021.
  53. Adafruit Camera, https://www.adafruit.com/products/613, 2021.

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