Wireless Sensors in IoT Based Agriculture by Using Block Chain Technology and Drones System

Authors

  • K. Durga Charan  Assistan Professor, CSE (Data Science) Department, Madanapalle Institute of Technology and Science, Madanapalle.
  • Afreen Subuhi  Assistant Professor, Department of IT, CMR College of Engineering and Technology, Hyderabad, Telangana, India
  • Potlacheruvu Archana  Asst Professor, CSE Department, Vignan Institute of Management and Technology for Women's, Hyderabad, Telangana, India
  • Karnati Durga  Assistant Professor, CSE (Data Science) Department, CMR Engineering College, Hyderabad, Telangana, India
  • B. Kumara Swamy  Assistant Professor, CSE (Data Science) Department, CMR Engineering College, Hyderabad, Telangana, India

DOI:

https://doi.org/10.32628/CSEIT239041

Keywords:

Crop management, sustainable agriculture, smart farming, internet-of-things (IoT), block chain technology, drones in agriculture.

Abstract

The integration of cutting-edge technologies such as Wireless Sensors, the Internet of Things (IoT), Blockchain, and Drones in agriculture has the potential to revolutionize the industry. This paper explores the application of these technologies in agriculture, focusing on the benefits they bring to the sector. We delve into the significance of Wireless Sensors, the IoT, Blockchain, and Drones in enhancing agricultural practices, resource management, data security, and overall productivity. We also examine the challenges and potential solutions for the widespread adoption of this integrated approach in agriculture.

References

  1. (2019). World Population Prospects 2019: Highlights United Nations, Department of Economic and Social Affairs, Population Division. [Online]. Available: https://population.un.org/wpp/Publications/Files/WPP2019_Highlights.pdf.
  2. (2019). The State of Food and Agriculture. Food and Agriculture Organization of the United Nations. [Online]. Available:http://www.fao.org/3/ca6030en/ca6030en.pdf
  3. M. Duque-Acevedo, L. J. Belmonte-Urena, F. J. Cortes-Garcia, and F. Camacho-Ferre, “Agricultural waste: Review of the evolution,approaches and perspectives on alternative uses,” Global Ecol. Conservation,vol. 22, Jun. 2020, Art. no. e00902.
  4. (2014). Food Wastage Footprint-Full Cost Accounting. Food and Agriculture Organization of the United Nations. [Online]. Available:http://www.fao.org/3/a-i3991e.pdf.
  5. J. Vora, S. Tanwar, S. Tyagi, N. Kumar, and J. J. P. C. Rodrigues,“Home-based exercise system for patients using IoT enabled smart speaker,” in Proc. IEEE 19th Int. Conf. e-Health Netw., Appl. Services (Healthcom), Dalian, China, Oct. 2017, pp. 1–6.
  6. V. Saiz-Rubio and F. Rovira-Más, “From smart farming towards agriculture 5.0: A review on crop data management,” Agronomy, vol. 10,no. 2, p. 207, Feb. 2020.
  7. U. Shafi, R. Mumtaz, J. García-Nieto, S. A. Hassan, S. A. R. Zaidi,and N. Iqbal, “Precision agriculture techniques and practices: From considerations to applications,” Sensors, vol. 19, no. 17, p. 3796,Sep. 2019.
  8. Srisruthi, S.; Swarna, N.; Ros, G.M.S.; Elizabeth, E. Sustainable agriculture using eco-friendly and energy efficient sensor technology.
  9. In Proceedings of the 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 20–21 May 2016; IEEE: Bangalore, India, 2016; pp. 1442–1446. [CrossRef]
  10. Brodt, S.; Six, J.; Feenstra, G.; Ingels, C.; Campbell, D. Sustainable Agriculture. Nat. Educ. Knowl. 2011, 3, 1.
  11. Obaisi, A.I.; Adegbeye, M.J.; Elghandour, M.M.M.Y.; Barbabosa-Pliego, A.; Salem, A.Z.M. Natural Resource Management and Sustainable Agriculture. In Handbook of Climate Change Mitigation and Adaptation; Lackner, M., Sajjadi, B., Chen, W.Y., Eds.;Springer: Cham, Switzerland, 2022. [CrossRef]
  12. Latake, P.T.; Pawar, P.; Ranveer, A.C. The Greenhouse Effect and Its Impacts on Environment. Int. J. Innov. Res. Creat. Technol.2015, 1, 333–337.5. Reddy, T.; Dutta.
  13. P.E. Colombo, E. Patterson, L.S. Elinder, A.K. Lindroos, U. Sonesson, N. Darmon, A. Parlesak, Optimizing School Food Supply: Integrating Environmental, Health, Economic, and Cultural Dimensions of Diet Sustainability with Linear Programming (2019)
  14. R. Casado-Vara, J. Prieto, F. De la Prieta, J.M. Corchado, How blockchain improves the supply chain: Case study alimentary supply chain. Proc. Comput. Sci. 134, 393–398 (2018).
  15. W. Chen, G. Feng, C. Zhang, P. Liu, W. Ren, N. Cao, J. Ding, Development and application of big data platform for garlic industry chain. Comput. Mater. Contin. 58(1), 229–248 (2019)
  16. Y.C. Choe, J. Park, M. Chung, J. Moon, Effect of the food traceability system for building trust: Price premium and buying behavior. Inf. Syst. Front. 11(2), 167–179 (2009).
  17. T.K. Dasaklis, F. Casino, C. Patsakis, Defining granularity levels for supply chain traceability based on IoT and blockchain, in Proceedings of the International Conference on Omni-Layer Intelligent Systems, (ACM, 2019), pp. 184–190.
  18. P. Praveen, B. Rama, An optimized clustering method to create clusters efficiently. J. Mech. Contin Math Sci, ISSN (Online): 2454-7190. 15(1), 339–348 (2020, January). ISSN (Print): 0973-8975. https://doi.org/10.26782/jmcms.2020.01.00027.
  19. Ravindra Changala, “Classification by Decision Tree Induction Algorithm to Learn Decision Trees from the class-Labeled Training Tuples” published in International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), ISSN: 2277 128X , Volume 2, Issue 4, April 2012.
  20. Ravindra Changala, “Decision Tree Induction Approach for Data Classification Using Peano Count Trees” published in International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), ISSN: 2277 128X, Volume 2, Issue 4, April 2012.
  21. A. Parikh, M.S. Raval, C. Parmar, S. Chaudhary, Disease detection and severity estimation in cotton plant from unconstrained images, in 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), (IEEE, 2016), pp. 594–601.
  22. B.F. Glunz, W.R. Pearson, A.F. Munoz, Method and system for creating 3D models from 2D data for building information modeling (BIM). U.S. Patent 9,817,922, issued 14 Nov 2017.
  23. B. Rama, P. Praveen, H. Sinha, T. Choudhury, A study on causal rule discovery with PC algorithm, in 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), Dubai, (2017), pp. 616–621. https://doi.org/10.1109/ICTUS.2017.8286083

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-08-30

Issue

Volume 9, Issue 4, July-August-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Durga Charan, Afreen Subuhi, Potlacheruvu Archana, Karnati Durga, B. Kumara Swamy, " Wireless Sensors in IoT Based Agriculture by Using Block Chain Technology and Drones System" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.33-42, July-August-2023. Available at doi : https://doi.org/10.32628/CSEIT239041