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Research Articles

Vol. 12 No. sp1 (2025): Recent Advances in Agriculture by Young Minds - II

Perceived benefits and challenges of IoT-based smart irrigation system: A case study from Tamil Nadu farmers

DOI
https://doi.org/10.14719/pst.8965
Submitted
19 April 2025
Published
26-07-2025 — Updated on 07-08-2025
Versions

Abstract

The increasing challenges of water scarcity, labour shortages and climate variability have necessitated the adoption of precision farming technologies in Indian agriculture. Among these innovations, adopting Internet of Things (IoT) technology in agriculture offers significant potential to revolutionize farming methods by increasing productivity, encouraging crop diversification, enhancing sustainability and reducing environmental impact. This study aims to evaluate the perceived benefits and challenges associated with adopting IoT-based smart irrigation systems by farmers in the Coimbatore district of Tamil Nadu. Data were collected from 120 farmers using IoT-based smart irrigation systems, randomly selected from the user list provided by the case firm. The Analytical Hierarchy Process (AHP) was employed to evaluate and prioritize the benefits perceived by farmers. At the same time the Plackett-Luce Model (PLM) was used to identify and rank the challenges that hinder effective usage. Results indicated that environmental advantages, particularly reduced water and energy consumption, were perceived as the most significant. Economic benefits, including enhanced operational efficiency and decreased labour costs, were also perceived as significant outcomes. Additionally, farmers acknowledged personal benefits like real-time monitoring, improved decision-making and reduced physical strain. Despite these advantages, high initial investment, complex user interfaces and limited technical knowledge were identified as the critical challenges encountered by the farmers. These findings suggest the need for affordable, user-friendly and locally supported IoT solutions and targeted capacity-building programs. Addressing these factors is essential for promoting broader adoption and realizing the full potential of smart irrigation technologies for sustainable agriculture.

References

  1. 1. González Perea R, Daccache A, Rodríguez Díaz J, Camacho Poyato E, Knox JW. Modelling impacts of precision irrigation on crop yield and in-field water management. Precision Agriculture. 2018;19:497-512. https://doi.org/10.1007/s11119-017-9535-4
  2. 2. World Sustainable Development Summit. Water – key facts for its sustainable management in India; 2022. Available from: https://www.teriin.org/sites/default/files/2021-06/water-factsheet.pdf
  3. 3. International Water Management Institute. India Water Week 2024 underscores collective action for water security; 2024 Available from: https://www.iwmi.org/news/key-takeaways-and-iwmis-contributions-at-india-water-week-2024/
  4. 4. Et-taibi B, Abid MR, Boufounas E-M, Morchid A, Bourhnane S, Hamed TA, et al. Enhancing water management in smart agriculture: A cloud and IoT-Based smart irrigation system. Results in Engineering. 2024;22:102283. https://doi.org/10.1016/j.rineng.2024.102283
  5. 5. Zhao W, Lin S, Han J, Xu R, Hou L. Design and implementation of smart irrigation system based on LoRa. In: 2017 IEEE Globecom Workshops (GC Wkshps). Singapore: IEEE; 2017. https://doi.org/10.1109/GLOCOMW.2017.8269115
  6. 6. Obaideen K, Yousef BA, AlMallahi MN, Tan YC, Mahmoud M, Jaber H, et al. An overview of smart irrigation systems using IoT. Energy Nexus. 2022;7:100124. https://doi.org/10.1016/j.nexus.2022.100124
  7. 7. Jha K, Doshi A, Patel P, Shah M. A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture. 2019;2:1-12. https://doi.org/10.1016/j.aiia.2019.05.004
  8. 8. García L, Parra L, Jimenez JM, Lloret J, Lorenz P. IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors. 2020;20(4):1042. https://doi.org/10.3390/s20041042
  9. 9. Vallejo-Gomez D, Osorio M, Hincapie CA. Smart irrigation systems in agriculture: A systematic review. Agronomy. 2023;13(2):342. https://doi.org/10.3390/agronomy13020342
  10. 10. Lee J. Evaluation of automatic irrigation system for rice cultivation and sustainable agriculture water management. Sustainability. 2022;14(17):11044. https://doi.org/10.3390/su141711044
  11. 11. Finch H. An introduction to the analysis of ranked response data. Practical Assessment, Research & Evaluation. 2022;27:7.
  12. 12. Agresti A. Categorical data analysis: John Wiley & Sons; 2013.
  13. 13. Jabbari A, Teli TA, Masoodi F, Reegu FA, Uddin M, Albakri A. Prioritizing factors for the adoption of IoT-based smart irrigation in Saudi Arabia: a GRA/AHP approach. Frontiers in Agronomy. 2024;6:1335443. https://doi.org/10.3389/fagro.2024.1335443

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