Study of Agriculture Using Drones in India: Evaluation of Feasibility, Impact, and Adoption Challenges

Year : 2025 | Volume : 01 | Issue : 02 | Page : 21 33
    By

    Kazi Kutubuddin Sayyad Liyakat,

  • Muskan Pathan,

  1. Professor, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Student, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

Studies in India show that drone technology is revolutionizing agriculture by enabling precision farming, increasing crop yields, reducing costs, and improving sustainability. Key applications include using drones for efficient spraying, crop monitoring via multispectral sensors, soil health analysis, and optimized water management. Government initiatives like the “Kisan Drones” program are promoting adoption, supported by research that demonstrates significant benefits like yield increases and resource savings[1-3]. Figure 1 shows the usage of drone in Agriculture.

Keywords: Drone, agriculture, crop yield, water usage, project garuda

[This article belongs to International Journal on Drones ]

How to cite this article:
Kazi Kutubuddin Sayyad Liyakat, Muskan Pathan. Study of Agriculture Using Drones in India: Evaluation of Feasibility, Impact, and Adoption Challenges. International Journal on Drones. 2025; 01(02):21-33.
How to cite this URL:
Kazi Kutubuddin Sayyad Liyakat, Muskan Pathan. Study of Agriculture Using Drones in India: Evaluation of Feasibility, Impact, and Adoption Challenges. International Journal on Drones. 2025; 01(02):21-33. Available from: https://journals.stmjournals.com/ijd/article=2025/view=230379


References

  1. Singh, R.; Singh, S. A Review of Indian-Based Drones in the Agriculture Sector: Issues, Challenges, and Solutions. Sensors 2025, 25, 4876. https://doi.org/10.3390/s25154876
  2. Katekar, V.; Cheruku, J.K. The Application of Drone Technology for Sustainable Agriculture in India. Agric. Res. J. 2022, 10, 352–365.
  3. Ayamga, M.; Akaba, S.; Nyaaba, A.A. Multifaceted Applicability of Drones: A Review. Forecast. Soc. Change 2021, 167, 120677.
  4. Dutta, G.; Goswami, P. Application of Drone in Agriculture: A Review. J. Chem. Stud. 2020, 8, 181–187.
  5. Kim, J.; Kim, S.; Ju, C.; Son, H.I. Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications. IEEE Access 2019, 7, 105100–105115.
  6. Shukla, R.; Chakraborty, A.; Joshi, P.K. Vulnerability of Agro-Ecological Zones in India under the Earth System Climate Model Scenarios. Adapt. Strateg. Glob. Change 2017, 22, 399–425.
  7. Guebsi, R.; Mami, S.; Chokmani, K. Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges. Drones 2024, 8, 686. https://doi.org/10.3390/drones8110686
  8. Chaturvedi, S.K.; Sekhar, R.; Banerjee, S.; Kamal, H. Comparative Review Study of Military and Civilian Unmanned Aerial Vehicles (UAVs). INCAS Bull. 2019, 11, 183–198.
  9. A. Tamboli, V. A. Sawant, M. H. M. and S. Sathe, (2024). AI-Driven-IoT(AIIoT) Based Decision-Making- KSK Approach in Drones for Climate Change Study, 2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS), Gobichettipalayam, India, 2024, pp. 1735-1744, doi: 10.1109/ICUIS64676.2024.10866450.
  10. SLiyakat, S. (2025l). AI-Driven-IoT (AIIoT)-Based Decision Making in Drones for Climate Change: KSK Approach. In S. Aouadni& I. Aouadni (Eds.), Recent Theories and Applications for Multi-Criteria Decision-Making (pp. 311-340). IGI Global. https://doi.org/10.4018/979-8-3693-6502-1.ch011
  11. Altaf O. Mulani, Arti Vasant Bang, Ganesh B. Birajadar, Amar B. Deshmukh, and Hemlata Makarand Jadhav, (2024). IoT Based Air, Water, and Soil Monitoring System for Pomegranate Farming, Annals of Agri-Bio Research. 29 (2): 71-86, 2024.
  12. Sayyad (2025b). AI-Powered IoT (AI IoT) for Decision-Making in Smart Agriculture: KSK Approach for Smart Agriculture. In S. Hai-Jew (Ed.), Enhancing Automated Decision-Making Through AI (pp. 67-96). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6230-3.ch003
  13. SLiyakat, S. (2024c). Machine Learning-Based Pomegranate Disease Detection and Treatment. In M. Zia Ul Haq & I. Ali (Eds.), Revolutionizing Pest Management for Sustainable Agriculture (pp. 469-498). IGI Global. https://doi.org/10.4018/979-8-3693-3061-6.ch019
  14. SLiyakat, S. (2024f). Artificial Intelligence (AI)-Driven IoT (AIIoT)-Based Agriculture Automation. In S. Satapathy & K. Muduli (Eds.), Advanced Computational Methods for Agri-Business Sustainability (pp. 72-94). IGI Global. https://doi.org/10.4018/979-8-3693-3583-3.ch005
  15. SLiyakat, S. (2025n). Transformation of Agriculture Effectuated by Artificial Intelligence-Driven Internet of Things (AIIoT). In J. Garwi, M. Dzingirai, & R. Masengu (Eds.), Integrating Agriculture, Green Marketing Strategies, and Artificial Intelligence (pp. 449-484). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-6468-0.ch015
Regular Issue Subscription Review Article
Volume 01
Issue 02
Received 27/10/2025
Accepted 28/10/2025
Published 30/10/2025
Publication Time 3 Days
227

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