Skip to main navigation menu Skip to main content Skip to site footer

Review Articles

Vol. 11 No. sp2 (2024): International conference on Multidisciplinary Approaches to SDGs: Life Sciences Perception

Implementation and adoption of smart technologies in agri-allied sectors

DOI
https://doi.org/10.14719/pst.3467
Submitted
1 March 2024
Published
14-07-2025
Versions

Abstract

Together with livestock, horticulture and fishing, India's agriculture industry has successfully met government production targets and broken records in nearly every commodity category. Unfortunately, these industries have accomplished the targets at the expense of the deterioration of natural resources and adverse impact on the environment. Besides, being a cornerstone of global sustenance, these industries face multifaceted challenges ranging from resource scarcity to climate variability. The inclusion of smart farming combined with drones, artificial intelligence, robotics, robotic agricultural bots, cloud computing, wireless sensor networks, expert systems and the Internet of Things (IoT) to bring an effective change can be a better alternative. Integrating these technologies into farming facilitates improved managerial decision-making for all the stakeholders, resulting in increased yield. The benefits of AI adoption are multifaceted, encompassing heightened efficiency, reduced environmental impact and improved crop quality. Furthermore, the burgeoning agriculture- tech sector has the potential to stimulate economic growth and job creation. Looking ahead, emerging trends in robotics, machine learning and the IoT signify a dynamic future for AI in agriculture, heralding a transformative era for the industry. The study utilizes Systematic Literature Review (SLR) method, guided by the PRISMA technique, to develop a conceptual framework. A total of 28 documents published between 2010 and 2024 are included in the analysis. This paper aims to explore the various AI trends in these sectors, while thoroughly analysing the role of these techniques, as well as their challenges and prospects.

References

  1. 1. Bandhana D, Sharma NA. IoT and its significance on smart technologies in the health sector-An extensive review. In: 2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). 2022. pp. 1–8. IEEE. https://doi.org/10.1109/CSDE56538.2022.10089253
  2. 2. Boursianis AD, Papadopoulou MS, Gotsis A, Wan S, Sarigiannidis P, Nikolaidis S, et al. Smart irrigation system for precision agriculture—The AREThOU5A IoT platform. IEEE Sensors J. 2020;21(16):17539–47. https://doi.org/10.1109/JSEN.2020.3033526
  3. 3. Sivakumar R, Prabadevi B, Velvizhi G, Muthuraja S, Kathiravan S, Biswajita M, et al. Internet of things and machine learning applications for smart precision agriculture. IoT Appl Computer; 2022. p. 135–65.
  4. 4. Karningsih PD, Kusumawardani R, Syahroni N, Mulyadi Y, Saad MSBM. Automated fish feeding system for an offshore aquaculture unit. In: IOP Conference Series: Materials Science and Engineering. 2021;1072(1):012073. https://doi.org/10.1088/1757-899X/1072/1/012073
  5. 5. Jeyabharathi D, Divyadharshini M, Haripriya SP. Smart fish feeding system based on fish feeding intensity. In: 2022 7th International Conference on Communication and Electronics Systems (ICCES). 2022. pp. 1491–96. IEEE. https://doi.org/10.1109/ICCES54183.2022.9835981
  6. 6. Liu X, Zhang C, Du K, Sha Z, Luo Y, Wang CF. Application of a web-based multi-factor intelligent precision feeding system for fish. Energy Environ Sci. 2023. p. 1–15. https://doi.org/10.1039/C8EE02656D
  7. 7. Boogaard FP, Rongen KSAH, Kootstra GW. Robust node detection and tracking in fruit-vegetable crops using deep learning and multi-view imaging. Biosys Eng. 2020;192:117–32. https://doi.org/10.1016/j.biosystemseng.2020.01.023
  8. 8. Khanna A, Kaur S. Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Comp Electr Agri. 2019;157:218–31. https://doi.org/10.1016/j.compag.2018.12.039
  9. 9. Rani R, Kaur G, Singh P. Smart soil monitoring system for smart agriculture. In: Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture. 2021. pp. 213–29. IGI Global. https://doi.org/10.4018/
  10. 978-1-7998-1722-2.ch013
  11. 10. Shaheb MR, Sarker A, Shearer SA. Precision agriculture for sustainable soil and crop management. In: Soil Science-Emerging Technologies, Global Perspectives and Applications. Intech Open. 2022.
  12. 11. Mbandi J. Soil data collection using wireless sensor networks and offsite visualization: case study of the innovative solutions for digital agriculture project in Kenya [Doctoral dissertation]. NM-AIST; 2021. https://doi.org/
  13. 10.21227/v2m5-qs54
  14. 12. Kumar SV, Parvathi SU. Computational intelligent systems for crop and soil monitoring through digital imaging: A survey. In: Gupta R, Jain A, Wang J, Bharti S, Patel S, editors. Artificial Intelligence Tools and Technologies for Smart Farming and Agriculture Practices; 2023. p. 1–21. https://doi.org/10.4018/978-1-6684-8516-3.ch001
  15. 13. Tiwari PS, Sahni RK, Kumar SP, Kumar V, Chandel NS. Precision agriculture applications in horticulture. Pantnagar J Res. 2019;17(1):1–10.
  16. 14. Kondratieva OV, Fedorov AD, Slinko OV, Voytyuk VA, Alekseeva SA. New solutions in the horticultural industry. In: IOP Conference Series: Earth and Environmental Science. 2022;1010(1):012103. https://doi.org/10.1088/1755-1315/10
  17. 10/1/012103
  18. 15. Kumar V, Jakhwal R, Chaudhary N, Singh S. Artificial intelligence in horticulture crops. Annals of Horti. 2023;16(1):72–79. https://doi.org/10.5958/0976-4623.2023.00014.2
  19. 16. Haokip SW. Advanced horticulture with Artificial intelligence (AHAI). 2020;2(2):365–69.
  20. 17. Abdullah MST, Mazalan L. Smart automation aquaponics monitoring system. JOIV: Int J Info Visual. 2016;6(1-2):256–63. https://doi.org/10.30630/joiv.6.1-2.925
  21. 18. Iqbal U, Li D, Akhter M. Intelligent diagnosis of fish behaviour using deep learning method. Fishes. 2022;7(4):201. https://doi.org/10.3390/fishes7040201
  22. 19. Aharwal B, Roy B, Meshram S, Yadav A. Worth of artificial intelligence in the epoch of modern livestock farming: A review. Agri Sci Digest-A Res J. 2023;43(1):1–9. https://doi.org/10.18805/ag.D-5355
  23. 20. Chimakurthi VNSS. Implementation of artificial intelligence policy in the field of livestock and dairy farm. Ameri J Trade Policy. 2019;6(3):113–18. https://doi.org/10.18034/ajtp.v6i3.591
  24. 21. Niloofar P, Francis DP, Lazarova-Molnar S, Vulpe A, Vochin MC, Suciu G, et al. Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges. Comp Electr Agri. 2021;190:106406. https://doi.org/10.1016/j.compag.2021.106406
  25. 22. Kumari P. 6 Smart animal livestock management using vision AI. Labelling Made Easy; 2023.
  26. 23. Prabha C, Pathak A. Enabling technologies in smart agriculture: A way forward towards future fields. International Conference on Advancement in Computation andComputer Technologies; 2023. p. 821–26. https://doi.org/10.1109/InCACCT57535.2023.10141722
  27. 24. Eli-Chukwu NC. Applications of artificial intelligence in agriculture: A review. Eng Technol Appl Sci Res. 2019;9(4). https://doi.org/10.48084/etasr.2756
  28. 25. de Souza JS, dos Reis JGM, da Cruz Correia PF, Rodrigues GS. A bibliometric overview over smart farming. Chem Proceed. 2022;10(1):28. https://doi.org/10.3390/IOCAG2022-12327
  29. 26. Swami S. Innovative soil-crop management systems for climate-smart sustainable agriculture. J Environ Bio. 2023;44(3):I-II.
  30. 27. Jouini O, Sethom K, Bouallegue R. Wheat leaf disease detection using CNN in smart agriculture. International Wireless Communications and Mobile Computing; 2023. p. 1660–65. https://doi.org/10.1109/IWCMC58020.2023.10
  31. 183348
  32. 28. Suriya DS, Latha AH. Weeds classification using convolutional neural network architectures. J Soft Comp Parad. 2023;5(2):116–33. https://doi.org/10.36548/jscp.2023.2.003
  33. 29. Doddamani PK, Revathi GP. Detection of weed and crop using YOLO v5 algorithm. In: 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon). 2022. pp. 1–5. IEEE. https://doi.org/10.1109/MysuruCon55714.2022.
  34. 9972386
  35. 30. Osroosh Y, Peters RT, Campbell CS, Zhang Q. Comparison of irrigation automation algorithms for drip-irrigated apple trees. Computers and Electronics in Agriculture. 2026;128:87–99. https://doi.org/10.1016/j.compag.2016.08.013
  36. 31. Agrawal KN, Singh K, Tiwari PS, Chandra MP. Laser sensor-based tractor mounted herbicide applicator. In: Proceedings 2012 national conference on agro-informatics and precision agriculture (AIPA). 2012. pp. 183–85.
  37. 32. Tewari VK, Kumar AA, Nare B, Prakash S, Tyagi A. Microcontroller-based roller contact type herbicide applicator for weed control under row crops. Computers and Electronics in Agriculture. 2014;104:40–45. https://doi.org/10.10
  38. 16/j.compag.2014.03.005
  39. 33. Zhao Y, Gong L, Huang Y, Liu C. A review of key techniques of vision-based control for harvesting robot. Computers and Electronics in Agriculture. 2016;127:311–23. https://doi.org/10.1016/j.compag.2016.06.022
  40. 34. Das J, Cross G, Qu C, Makineni A, Tokekar P, Mulgaonkar Y, Kumar V. Devices, systems and methods for automated monitoring enabling precision agriculture. In: IEEE International Conference on Automation Science and Engineering. 2015. p. 462–69. https://doi.org/10.1109/CoASE.2015.7294123
  41. 35. Ya’acob N, Dzulkefli NNSN, Yusof AL, Kassim M, Naim NF, Aris SSM. Water quality monitoring system for fisheries using Internet of Things (IoT). Conference Series: Materials Sci Eng. 2021;1176(1):012016. https://doi.org/10.1088/
  42. 1757-899X/1176/1/012016
  43. 36. Yasruddin ML, Ismail MAH, Husin Z, Tan WK. Feasibility study of fish disease detection using computer vision and deep convolutional neural network (dcnn) algorithm. In: IEEE 18th International Colloquium on Signal Processing and Applications. 2022. p. 272–76. https://doi.org/10.1109/CSPA55076.2022.9782020
  44. 37. Endo H, Wu H. Biosensors for the assessment of fish health: a review. Fisheries Sci. 2019;85:641–54. https://doi.org/10.1007/s12562-019-01318-y
  45. 38. Tedeschi LO, Greenwood PL, Halachmi I. Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. J Animal Sci. 2021;99(2):1–11. https://doi.org/10.1093/jas/skab038
  46. 39. Bloch V, Levit H, Halachmi I. Assessing the potential of photogrammetry to monitor feed intake of dairy cows. J Dairy Res. 2019;86(1):34–39. https://doi.org/10.1017/S0022029918000882
  47. 40. Khanikar D, Phookan A, Gogoi A. Artificial neural networks- An introduction and application in animal breeding and production: A review. Agricultural Reviews. 2022. p. 1–8. https://doi.org/10.18805/ag.R-2421
  48. 41. Alsaaod M, Fadul M, Steiner A. Automatic lameness detection in cattle. The Vet J. 2019;246:35–44. https://doi.org/
  49. 10.1016/j.tvjl.2019.01.005
  50. 42. Murugeswari S, Murugan K, Rajathi S, Kumar MS. Monitoring body temperature of cattle using an innovative infrared photodiode thermometer. Computers and Electronics in Agriculture. 2022;198:107120. https://doi.org/10.10
  51. 16/j.compag.2022.107120
  52. 43. Singh A. Role of biometric identification in cattle management. Mantra Innovation That Counts; 2020.

Downloads

Download data is not yet available.