Smart Sensors and IoT Devices for Precision Agriculture

Authors

DOI:

https://doi.org/10.55938/wlp.v1i2.119

Keywords:

Precision Agriculture, Autonomous Agricultural Robot, Fossil Fuels, Crop Moisture Levels

Abstract

Agriculture, which is essential to the world economy and human life, has developed from conventional practices to more innovative methods like precision farming as a result of population growth and resource scarcity, which have led to increased production and utilization of resources.The objective of the study is to optimize crop yields by creating an autonomous agricultural robot that employs an Internet of Things (IoT) module to perform responsibilities like irrigating, seeding, and ploughing. IoT allows for almost real-time data collection from large networks, connecting wireless sensor networks (WSNs) and sensing a variety of information. This is particularly beneficial for row crop systems, which collect data from numerous sources. By integrating machine learning and artificial intelligence, precision agriculture (PA) employs technology to boost crop yield and solve issues including soil degradation, climate change, and growing expenditures. In addition to emphasizing its role in minimizing crop output gaps, food waste, and resource inefficiencies, this paper highlights the advantages of integrating ICT into precision agriculture for sustainable growth. By incorporating digital systems with machinery, Industry 4.0 is revolutionizing farming, particularly precision agriculture. This article includes a general overview of these systems and addresses how the changing environment of digital agriculture influences equipment design approaches. UAVs and sensors are employed in precision agriculture for detecting sickness, however their accuracy is limited. Classification and identification activities are performed by image processing software and machine learning models. However, successful application of these tools depends on the training and verification of databases. This article explores innovations in precision agriculture, especially technological breakthroughs like machine learning and drones. It also considers issues with data management, adoption of novel innovations, and cost-effectiveness. The growing demand for cloud computing can be attributed to advancements in processing and management. IoT and AI are promising productivity boosters. IoT data is readily available for research, revolutionizing conventional methods of cultivation and anticipating crop yield. Humanity may undergo profound transformation as a consequence of this.

 

References

1. Kim, B., Jang, J., Kim, S., Hwang, S., & Shin, M. (2021). Design of an ICT convergence farm machinery for an automatic agricultural planter. International Journal of Computational Vision and Robotics, 11(4), 448-460.

2. Karunathilake, E. M. B. M., Le, A. T., Heo, S., Chung, Y. S., &Mansoor, S. (2023). The path to smart farming: Innovations and opportunities in precision agriculture. Agriculture, 13(8), 1593.

3. Reis, Â. V. D., Medeiros, F. A., Ferreira, M. F., Machado, R. L. T., Romano, L. N., Marini, V. K., ... & Machado, A. L. T. (2021). Technological trends in digital agriculture and their impact on agricultural machinery development practices. RevistaCiênciaAgronômica, 51.

4. Ouafiq, E. M., Saadane, R., &Chehri, A. (2022). Data management and integration of low power consumption embedded devices IoT for transforming smart agriculture into actionable knowledge. Agriculture, 12(3), 329.

5. Raj, E. F. I., Appadurai, M., &Athiappan, K. (2022).Precision farming in modern agriculture. In Smart Agriculture Automation Using Advanced Technologies: Data Analytics and Machine Learning, Cloud Architecture, Automation and IoT (pp. 61-87). Singapore: Springer Singapore.

6. Gorjian, S., Minaei, S., MalehMirchegini, L., Trommsdorff, M., &Shamshiri, R. R. (2020).Applications of solar PV systems in agricultural automation and robotics.In Photovoltaic Solar Energy Conversion (pp. 191-235).Academic Press.

7. Venkatesh, B., Suresh, Y., ChinnaBabu, J., Guru Mohan, N., Madana Kumar Reddy, C., & Kumar, M. (2023). Design and implementation of a wireless communication-based sprinkler irrigation system with seed sowing functionality. SN Applied Sciences, 5(12), 1-11.

8. Singh, R. K., Berkvens, R., &Weyn, M. (2021).AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey. IEEE Access, 9, 136253-136283.

9. Chaterji, S., DeLay, N., Evans, J., Mosier, N., Engel, B., Buckmaster, D., ...& Chandra, R. (2021). Lattice: A vision for machine learning, data engineering, and policy considerations for digital agriculture at scale. IEEE Open Journal of the Computer Society, 2, 227-240.

10. Sengodan, P., &Jbara, Y. H. F. (2017). Development of IoT controlled agri-rover for automatic seeding. International Journal of Pure and Applied Mathematics, 114(11), 241-251.

11. Mahmud, M. S. A., Abidin, M. S. Z., Emmanuel, A. A., &Hasan, H. S. (2020). Robotics and automation in agriculture: present and future applications. Applications of Modelling and Simulation, 4, 130-140.

12. Verma, P., Bhutani, S., Srividhya, S., KARTHIKEYAN, D., & TONG, D. C. S. (2019). Review of internet of things towards sustainable development in agriculture. Journal of Critical Reviews, 7(3), 2020.

13. Bőgel, G. (2017). Competing in a smart world: the need for digital agriculture.

14. Chaterji, S., DeLay, N., Evans, J., Mosier, N., Engel, B., Buckmaster, D., & Chandra, R. (2020). Artificial intelligence for digital agriculture at scale: Techniques, policies, and challenges. arXiv preprint arXiv:2001.09786.

15. Dewangan, A. K. (2020). Application of IoT and machine learning in agriculture. Int. J. Eng. Res. Technol.(IJERT), 9(7).

16. Poovammal, E. (2021, September). Intelligent Greenhouse cultivation empowered in IoT ecosystem. In 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) (pp. 141-146).IEEE.

17. Charania, I., & Li, X. (2020). Smart farming: Agriculture's shift from a labor intensive to technology native industry. Internet of Things, 9, 100142.

18. Alsamhi, S. H., Ma, O., Ansari, M. S., &Meng, Q. (2019). Greening internet of things for greener and smarter cities: a survey and future prospects. Telecommunication Systems, 72, 609-632.

19. Poongodi, T., Ramya, S. R., Suresh, P., &Balusamy, B. (2020).Application of IoT in green computing. Advances in Greener Energy Technologies, 295-323.

20. Hamrouni, B., Abid, R., &Niou, A. SMARTAGRI: An Intelligent Decision Support System for Smart Farming (Doctoral dissertation, University of KasdiMerbahOuargla).

21. Neupane, K., &Baysal-Gurel, F. (2021). Automatic identification and monitoring of plant diseases using unmanned aerial vehicles: A review. Remote Sensing, 13(19), 3841.

22. Gupta, V. K., & Ahmed, M. B. (2021). Internet of Things: Submissions in the field of Farming using Machine Learning.

23. Malode, S. M., Telang, S., Gangane, S., Randai, A., &Meshram, H. AUTOMATIC SEED SOWING SYSTEM.

24. Sri, P. B., Gayathri, C. H., Sai, K. A. R. S., Ramya, C. J. S., &Teja, E. V. IOT AGRICULTURAL ROBOT FOR AUTOMATIC PLOUGHING, SEEDING AND SPRINKILING.

25. Yadav, G. K., Dadhich, S. K., &Bhateshwar, M. C.Recent Innovative Approaches in Agricultural Science.

26. Alahmad, T., Neményi, M., &Nyéki, A. (2023). Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review. Agronomy, 13(10), 2603.

Published

2024-11-21

How to Cite

Pramanik, A., Bisht, K., & Singh, D. (2024). Smart Sensors and IoT Devices for Precision Agriculture. Wisdom Leaf Press, 1(2), 99–103. https://doi.org/10.55938/wlp.v1i2.119

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