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Farmers are facing the VUCA environment (volatile, uncertain, complex and ambiguous) and data indicating the contribution of farming to India's GDP has come down from 52% to 18% between 1951 and 2018, which is alarming. At this juncture, developing countries like India, where over 70% of the rural people depend upon the agriculture fields, adoption of disruptive technology (creative destruction) becomes the need of the hour, to enhance the crop yield and quality. Weeds are one of the major issues which severely affect the crop output. Unmanned Aerial Vehicle (UAV) or drone is recommended, to address the problem. Globally, the market for agriculture drones to move from $1.3 billion to $ 6.52 billion by 2026. Globally agriculture is the second largest industry after construction in terms of drone adoption. But Indian farmers have difficulty in adopting (or) procuring UAV's, as the size of their farm is small, income is very less. Other problems associated with the adoption of UAV include knowledge transfer and training to farmers, service support and maintenance cost. DaaS (Drone as a service) model is proposed, for rural areas. This paper aims to focus on weed management by providing a safer and cost-effective solution. By integrating technologies like visible light (VIS), near-infrared (NIR) light on an Unmanned Ariel Vehicle along with a precise sprayer and a weed detection system backed up by a lithium-ion battery (for longer flight duration), can help the process of spraying weedicide efficiently. The accuracy of the tested model is 92.6% for far away detection module and 95.4 for close range detection. UAV's with sprayer protects the farmer and consumers from odour and side effects.


Drones KNN OpenCV Agriculture 5.0 United Nations Sustainable Development

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How to Cite
Prabhu, S. S. ., Kumar, . A. .Vishal ., Murugesan, R. ., Saha, J. ., & Dasgupta, . I. . (2021). Adoption of Precision Agriculture by Detecting and Spraying Herbicide using UAV. Basrah Journal of Agricultural Sciences, 34, 21–33.


  1. Albani, D., Nardi, D., & Trianni, V. (2017). Field coverage and weed mapping by UAV swarms. IEEE. hors
  2. Ram Swaroop Meena, Sandeep Kumar, Rahul Datta, Rattan Lal, et al., (2020), Impact of Agrochemicals on Soil Microbiota and Management: A Review .MDPI , Land
  3. Bob Reiter, R. (2019). The crop protection toolbox: How farmers defend their crops from monster weeds Forbes.
  4. Carbone, C., Garibaldi, O., & Kurt, Z. (2018). Swarm robotics as a solution to crops inspection for precision agriculture”, ESTEC Conference Proceedings 6th Engineering, Science and Technology Conference. 2018. 552-562. file:///C:/Users/hassan /Downloads /1459-Article%20Text-7799-1-10-20180211.pdf
  5. Daphne Ewing - Chow, (2020). Wild Waste: It’s time to rethink the war on weeds, Forbes.
  6. Duffy, J. P., Cunliffe A. M., & De Bell, L. (2017). Location location location considerations when using lightweight drones in challenging environments, Remote Sensing in Ecology and Conservation, 1-13.
  7. Gómez-Candón, D. ,De Castro A.I., López-Granados F. (2014). Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat”, IEEE Conference on Computer Vision and Pattern Recognition. Precision Agriculture, 15, 44-56.
  8. Kale, S. D., Khandagale , S. V., Gaikwad, S. S., Narve, S. S., & Gangal, P. V. (2015). Agriculture Drone for Spraying Fertilizer and Pesticides International Journal of Advanced Research in Computer Science and Software Engineering, 5, 804-807. https://www.
  9. Lan, Y., & Chen, Sh. (2018) Current status and trends of plant protection UAV and its spraying technology in China”, International Journal of Precision Agricultural Aviation, 1, 1-9. https://pdfs. Semantic
  10. Meng, Y., Su, J., Song, J., Chen, W., & Lan, Y. (2020). Experimental evaluation of UAV spraying for peach trees of different shapes: Effects of operational parameters on droplet distribution, Computers and Electronics in Agriculture 170, https://doi. Org /10. 1016 /j.compag.2020.105282
  11. Murugesan, R., & Sudarsanam, S. K. (2019). Transdisciplinary Approach for Sustainable Rural Development, IJRTE, 8, 2453-2460 https://www. uploads /papers /v8i1 /A222905 8119.pdf
  12. Murugesan, R., & Sudarsanam, S. K. (2020). Development of smarty farming framework. Test Engineering and Management, 83, 8474 – 8484.
  13. Murugesan, R., Sudarsanam, S. K., & Shanmugasundaram, S. (2018). Industry 4.0 for sustainable development. Annual Technical Volume of the Institution of Engineers, 3.
  14. Murugesan, R., Sudarsanam, S. K., Malathi. G., & Varadarajan, V. Venkataraman, N., Venugopal, R., Rekha, D., Saha, S., Bajaj, R., Miral, A., & Venkataraghavan, M. (2019). Artificial Intelligence and Agriculture 5.0, IJRTE
  15. Partel, V., Kakarla, S. Ch., & Ampatzidis, Y. (2019) Development and evaluation of a low - cost and smart technology for precision weed management utilizing artificial intelligence Computers and Electronics in Agriculture, 157, 339-350.
  16. Perez, G. (2020). Weed spotting by drone, UC Weed Science. detail .cfm?postnum=25759
  17. Rosenthal, A. (2018). Drones for development: Ho UAV’s are supporting the global goals, UN Foundation, nes-for-development-how-uavs-are-supporting-the-global-goals/
  18. Schumpeter, J. A (1942). Capitalism, Socialism and democracy. Harper & Brothers, 431pp.
  19. Shakhatreh, H., Sawalmeh, A., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. Sh., Khreishah, A., & Guizani, M. (2019). Unmanned Aerial Vehicles (UAVs): A survey on civil applications and key research challenge. In IEEE Access, vol. 7, pp. 48572-48634. doi: 10.1109/ACCESS.2019.2909530.
  20. Sutton, R. S., & Barto, A. (1998), Reinforcement learning, MIT Press, 344pp. https://www.Andrew /textbook/B arto Sut ton .pdf
  21. Tan, Y., & Zheng, Z. Y. (2013). Research Advance in Swarm Robotics, Defense Technology, 9, 18-39,
  22. Torres-Sánchez, J., López-Granados, F., De Castro, A. I., & Peña-Barragán, J. M. (2013). Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management. PloS one, 8, e58210. https://doi.Org/10.1371/journal.pone.0058 210