Developing Smart Farming Solutions: IoT-Based Techniques for Precision Agriculture

Volume: 10 | Issue: 1 | Year 2024 | Subscription
International Journal of Electrical Power System and Technology
Received Date: 05/24/2024
Acceptance Date: 06/12/2024
Published On: 2024-06-28
First Page: 30
Last Page: 36

Journal Menu

By: Pratidnya Dnyanoba Shinde, M. B. Mali, and M. N. Namewar

1-ME student, Department of Electronics & Telecommunication Engineering, Sinhgad college of Engineering, Pune, Maharashtra, India
2-Head of Department, Department of Electronics & Telecommunication Engineering, Sinhgad college of Engineering, Pune, Maharashtra, India
3-Assistant Professor, Department of Electronics & Telecommunication Engineering, Sinhgad college of Engineering, Pune, Maharashtra, India

Abstract

A precision agricultural system is a smart farm management system that minimizes production costs while maximizing productivity and profitability through the identification, analysis, and management of field variability using information and technology. Because of the public’s increased environmental consciousness, we must alter agricultural management practices to assure economic success while safeguarding natural resources like water, air, and soil quality. With the introduction of Internet of Things (IoT) technology, precision agriculture holds the potential to significantly transform the agricultural industry. It uses a variety of technologies, including automation, networking, sensors, and data analytics, to improve crop output, manage resources more efficiently, and advance sustainable agricultural methods. This abstract summarizes the key benefits of precision agriculture systems and helps farmers boost crop yields by providing them with information on soil conditions and crop health. By using real-time data to inform their decisions, systems help farmers maximize their resources and increase crop yields. It supports sustainable farming techniques by carefully regulating inputs like water, fertilizer, and pesticides, thereby minimizing environmental effect.

Keywords: Advanced agriculture, Efficiency, Internet of Things (IoT), Remote sensing, Smart farming, Irrigation management

Loading

Citation:

How to cite this article: Pratidnya Dnyanoba Shinde, M. B. Mali, and M. N. Namewar, Developing Smart Farming Solutions: IoT-Based Techniques for Precision Agriculture. International Journal of Electrical Power System and Technology. 2024; 10(1): 30-36p.

How to cite this URL: Pratidnya Dnyanoba Shinde, M. B. Mali, and M. N. Namewar, Developing Smart Farming Solutions: IoT-Based Techniques for Precision Agriculture. International Journal of Electrical Power System and Technology. 2024; 10(1): 30-36p. Available from:https://journalspub.com/publication/developing-smart-farming-solutions-iot-based-techniques-for-precision-agriculture/
Full Text

Refrences:

  1. Alfred, R.; Obit, J.H.; Chin, C.P.-Y.; Haviluddin, H.; Lim, Y. Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks. IEEE Access2021, 9, 50358–50380. [Google Scholar] [CrossRef]
  2. McFadden, J.; Njuki, E.; Griffin, T. Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms. US Department of Agriculture, Economic Research Service 248. 2023. Available online: https://www.ers.usda.gov(accessed on 2 March 2023).
  3. Monteiro, A.; Santos, S.; Gonçalves, P. Precision Agriculture for Crop and Livestock Farming—Brief Review. Animals2021, 11, 2345. [Google Scholar] [CrossRef] [PubMed]
  4. Bhat, S.A.; Huang, N.-F. Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE Access2021, 9, 110209–110222. [Google Scholar] [CrossRef]
  5. Yazdinejad, A.; Zolfaghari, B.; Azmoodeh, A.; Dehghantanha, A.; Karimipour, H.; Fraser, E.; Green, A.G.; Russell, C.; Duncan, E. A Review on Security of Smart Farming and Precision Agriculture: Security Aspects, Attacks, Threats, and Countermeasures.  Sci.2021, 11, 7518. [Google Scholar] [CrossRef]
  6. Cravero, A.; Sepúlveda, S. Use and Adaptations of Machine Learning in Big Data—Applications in Real Cases in Agriculture. Electronics2021, 10, 552. [Google Scholar] [CrossRef]
  7. Filipe, J.; Śmiałek, M.; Brodsky, A.; Hammoudi, S. (Eds.) Enterprise Information Systems: 21st International Conference, ICEIS 2019, Heraklion, Crete, Greece, 3–5 May 2019, Revised Selected Papers. In Lecture Notes in Business Information Processing; Springer International Publishing: Cham, Switzerland, 2020; Volume 378. [Google Scholar] [CrossRef]
  8. Liu, Y.; Ma, X.; Shu, L.; Hancke, G.P.; Abu-Mahfouz, A.M. From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges. IEEE Trans. Ind. Inform.2021, 17, 4322–4334. [Google Scholar] [CrossRef]
  9. Trivelli, L.; Apicella, A.; Chiarello, F.; Rana, R.; Fantoni, G.; Tarabella, A. From precision agriculture to Industry 4.0: Unveiling technological connections in the agrifood sector.  Food J.2019, 121, 1730–1743. [Google Scholar] [CrossRef]
  10. Shin, J.; Mahmud, S.; Rehman, T.U.; Ravichandran, P.; Heung, B.; Chang, Y.K. Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture. Agriengineering2022, 5, 20–39. [Google Scholar] [CrossRef]
  11. Haneklaus, S.; Lilienthal, H.; Schnug, E. 25 years Precision Agriculture in Germany—A retrospective. In Proceedings of the 13th International Conference on Precision Agriculture, St. Louis, MO, USA, 31 July 31–4 August 2016. [Google Scholar]
  12. Hedley, C. The role of precision agriculture for improved nutrient management on farms: Precision agriculture managing farm nutrients.  Sci. Food Agric.2015, 95, 12–19. [Google Scholar] [CrossRef]
  13. Hundal, G.S.; Laux, C.M.; Buckmaster, D.; Sutton, M.J.; Langemeier, M. Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices. Agriculture2023, 13, 163. [Google Scholar] [CrossRef]
  14. Ma, M.; Liu, J.; Liu, M.; Zeng, J.; Li, Y. Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains. Forests2021, 12, 1736. [Google Scholar] [CrossRef]
  15. Sendros, A.; Drosatos, G.; Efraimidis, P.S.; Tsirliganis, N.C. Blockchain Applications in Agriculture: A Scoping Review.  Sci.2022, 12, 8061. [Google Scholar] [CrossRef]
  16. Javaid, M.; Haleem, A.; Singh, R.P.; Suman, R. Enhancing smart farming through the applications of Agriculture 4.0 technologies.  J. Intell. Netw.2022, 3, 150–164. [Google Scholar] [CrossRef]
  17. Chung, Y.S.; Yoon, S.U.; Heo, S.; Kim, Y.S.; Ahn, J.; Han, G.D. Characterization of Tree Composition using Images from SENTINEL-2: A Case Study with Semiyang Oreum.  Environ. Sci. Int.2022, 31, 735–741. [Google Scholar] [CrossRef]
  18. Marr, B. Tech Trends in Practice: The 25 Technologies That Are Driving the 4th Industrial Revolution; John Wiley & Sons: Chichester, UK, 2020. [Google Scholar]
  19. Hegde, P. Precision Agriculture: How Is It Different from Smart Farming? Croping. 24 September 2021. Available online: https://www.cropin.com/blogs/smart-farming-vs-precision-farming-systems(accessed on 10 June 2023).
  20. In Brief to the State of Food and Agriculture 2022. Leveraging Automation in Agriculture for Transforming Agrifood Systems; FAO: Rome, Italy, 2022. [Google Scholar] [CrossRef]
  21. Lund, E.D.; Maxton, C.R.; Lund, T.J. A Data Fusion Method for Yield and Soil Sensor Maps; International Society of Precision Agriculture: Monticello, IL, USA, 2016. [Google Scholar]
  22. Jang, G.; Kim, D.-W.; Kim, H.-J.; Chung, Y.S. Short Communication: Spatial Dependence Analysis as a Tool to Detect the Hidden Heterogeneity in a Kenaf Field. Agronomy2023, 13, 428. [Google Scholar] [CrossRef]
  23. Martin, D.E.; Yang, C. Field Evaluation of a Variable-Rate Aerial Application System; International Society of Precision Agriculture: Monticello, IL, USA, 2016. [Google Scholar]
  24. Walter, A.; Finger, R.; Huber, R.; Buchmann, N. Opinion: Smart farming is key to developing sustainable agriculture.  Natl. Acad. Sci. USA2017, 114, 6148–6150. [Google Scholar] [CrossRef] [PubMed]
  25. Punithavathi, R.; Rani, A.D.C.; Sughashini, K.R.; Kurangi, C.; Nirmala, M.; Ahmed, H.F.T.; Balamurugan, S.P. Computer Vision and Deep Learning-enabled Weed Detection Model for Precision Agriculture.  Syst. Sci. Eng.2023, 44, 2759–2774. [Google Scholar] [CrossRef]

 Previous Article
Next Article