AI and IoT in Sustainable Agriculture: A Review

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Year : 2025 | Volume : 15 | Issue : 02 | Page : –
    By

    Rohit Sandip Birdawade,

  • Shreya Sanjay Bhosale,

  • Athrava Gajanan Dabhole,

  • Niranjan Shrikrishna Bansode,

  • Sanjay Bapuso Patil,

  1. student, Department of Computer Engineering, Dnyanpeeth’s SCSCOE Pune, Maharashtra, India
  2. student, Department of Computer Engineering, Dnyanpeeth’s SCSCOE Pune, Maharashtra, India
  3. student, Department of Computer Engineering, Dnyanpeeth’s SCSCOE Pune, Maharashtra, India
  4. student, Department of Computer Engineering, Dnyanpeeth’s SCSCOE Pune, Maharashtra, India
  5. Student, Department of Computer Engineering, Rajgad Dnyanpeeth’s SCSCOE, Pune, Maharashtra, India

Abstract

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Artificial Intelligence (AI) and Internet of Things (IoT) integration have transformed the world of sustainable agriculture, presenting new ways of resource optimization, increasing crop yields, and making environmental sustainability more accessible. The current literature review analyzes the applications of AI and IoT in three significant agricultural systems: aquaponics, hydroponics, and poultry farming. By critically analyzing recent studies, this paper emphasizes how deep learning- enabled computer vision techniques allow for the precise monitoring and management of greenhouse environments by overcoming issues like data scarcity and environmental variability. In addition, the review focuses on IoT-driven advancements in hydroponic and aquaponic systems, focusing on sensor integration, real-time data processing, and machine learning algorithms that predict nutrient requirements and optimize growth conditions. In the poultry farming context, the use of AI models in environmental control and disease detection can contribute to the reduction of labor dependency and the enhancement of animal welfare. The identified challenges include the need for domain-specific AI adaptations, robust security protocols for IoT infrastructures, and scalability of integrated systems. It further indicates future research directions, such as multi-modal sensor fusion, edge computing for real-time analytics, and user-friendly platforms that would make it accessible to a wider audience among farmers. Synthesizing the current advancements and the gaps in them, this paper provides
researchers and practitioners with insights into how to utilize AI and IoT in agriculture for sustainability and efficiency.

Keywords: Artificial intelligence (AI), internet of things (IoT), sustainable agriculture, aquaponics, hydroponics, poultry farming, smart greenhouses, machine learning, precision agriculture, IoT-driven systems

[This article belongs to Journal of Instrumentation Technology & Innovations ]

How to cite this article:
Rohit Sandip Birdawade, Shreya Sanjay Bhosale, Athrava Gajanan Dabhole, Niranjan Shrikrishna Bansode, Sanjay Bapuso Patil. AI and IoT in Sustainable Agriculture: A Review. Journal of Instrumentation Technology & Innovations. 2025; 15(02):-.
How to cite this URL:
Rohit Sandip Birdawade, Shreya Sanjay Bhosale, Athrava Gajanan Dabhole, Niranjan Shrikrishna Bansode, Sanjay Bapuso Patil. AI and IoT in Sustainable Agriculture: A Review. Journal of Instrumentation Technology & Innovations. 2025; 15(02):-. Available from: https://journals.stmjournals.com/joiti/article=2025/view=0

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References

1. Rahimzadeh Karvansara P, Kelly C, Krone R, Zenzen I, Ristova D, Silz E, Jobe TO, Kopriva S.
Unique features of regulation of sulfate assimilation in monocots. Journal of Experimental Botany.
2023 Jan 1;74(1):308–20.
2. Debdas S, Jain A, Mariam A, Maiti A, Borah A. Enhancing Hydroponic Systems with IoT:
Optimizing pH, Temperature, and Humidity Parameters. In2024 3rd Odisha International
Conference on Electrical Power Engineering, Communication and Computing Technology
(ODICON) 2024 Nov 8 (pp. 1–7). IEEE.
3. Channa AA, Munir K, Hansen M, Tariq MF. Optimisation of small-scale aquaponics systems using
artificial intelligence and the IoT: current status, challenges, and opportunities. Encyclopedia. 2024
Feb 8;4(1):313–36.
4. Prasad R, Prashanth P, Srikanth R. AI Based Smart Hydroponics System. In2024 IEEE
International Conference on Information Technology, Electronics and Intelligent Communication
Systems (ICITEICS) 2024 Jun 28 (pp. 1–5). IEEE.
5. Laddha SV, Shastrakar PP, Zade SA. A survey on smart hydroponics farming: An integration of
IoT and AI-–based efficient alternative to land farming. InInternational Conference on Frontiers of
Intelligent Computing: Theory and Applications 2023 Apr 11 (pp. 121–130). Singapore: Springer
Nature Singapore.
6. Krishnan A, Swarna S. Robotics, IoT, and AI in the automation of agricultural industry: a review.
In2020 IEEE Bangalore Humanitarian Technology Conference (B-HTC) 2020 Oct 8 (pp. 1–6).
IEEE.
7. Sharma A, Jain A, Gupta P, Chowdary V. Machine learning applications for precision agriculture:
A comprehensive review. IEEe Access. 2020 Dec 31;9:4843–73.
8. Sharma A, Jain A, Gupta P, Chowdary V. Machine learning applications for precision agriculture:
A comprehensive review. IEEe Access. 2020 Dec 31;9:4843–73.
9. A. Z. Babar and O. B. Akan, “Sustainable and Precision Agriculture with the Internet of Everything
(IoE),” arXiv preprint arXiv:2404.06341, Apr. 2024. doi: 10.48550/arXiv.2404.06341.
10. Vegesna VV, Prabhakaran SM, Whig P. AI for Food Safety: Leveraging Artificial Intelligence to
Ensure a Safe and Reliable Food Supply. InNutrition Controversies and Advances in Autoimmune
Disease 2024 (pp. 501–524). IGI Global Scientific Publishing.
11. Akbar JU, Kamarulzaman SF, Muzahid AJ, Rahman MA, Uddin M. A comprehensive review on
deep learning assisted computer vision techniques for smart greenhouse agriculture. IEEE Access.
2024 Jan 3;12:4485–522.
12. Farooq MS, Riaz S, Helou MA, Khan FS, Abid A, Alvi A. Internet of things in greenhouse
agriculture: a survey on enabling technologies, applications, and protocols. IEEE access. 2022 Apr
11;10:53374–97.
13. Su F, Zhao Y, Wang G, Liu P, Yan Y, Zu L. Tomato maturity classification based on SE-YOLOv3-
MobileNetV1 network under nature greenhouse environment. Agronomy. 2022 Jul 8;12(7):1638.
14. Chen M, Lang X, Zhai X, Li T, Shi Y. Intelligent recognition of greenhouse cucumber canopy vine
top with deep learning model. Computers and Electronics in Agriculture. 2023 Oct 1;213:108219.
15. Cong P, Li S, Zhou J, Lv K, Feng H. Research on instance segmentation algorithm of greenhouse
sweet pepper detection based on improved mask RCNN. Agronomy. 2023 Jan 7;13(1):196.
16. Shinoda R, Motoki K, Hara K, Kataoka H, Nakano R, Nakazaki T, Noguchi R. RoseTracker: A
system for automated rose growth monitoring. Smart Agricultural Technology. 2023 Oct
1;5:100271.
17. Verma MS, Gawade SD. A machine learning approach for prediction system and analysis of
nutrients uptake for better crop growth in the Hydroponics system. In2021 international conference
on artificial intelligence and smart systems (ICAIS) 2021 Mar 25 (pp. 150–156). IEEE.

18. Kondaka LS, Iyer R, Jaiswal S, Ali A. A smart hydroponic farming system using machine learning.
In2023 International Conference on Intelligent and Innovative Technologies in Computing,
Electrical and Electronics (IITCEE) 2023 Jan 27 (pp. 357–362). IEEE.
19. Patil SP, Mathews LM, Kumar A, Motgi SB, Sinha U. Ai-driven hydroponic systems for lemon
basil. In2023 International Conference on Network, Multimedia and Information Technology
(NMITCON) 2023 Sep 1 (pp. 1–6). IEEE.
20. Arora U, Shetty S, Shah R, Sinha DK. Automated dosing system in hydroponics with machine
learning. In2021 International Conference on Communication information and Computing
Technology (ICCICT) 2021 Jun 25 (pp. 1–6). IEEE.
21. Malarvizhi P, Dayana R, Srivathsan D, Varman SK, Shashank V. Integrated Smart Farming
Technique Using IOT and AI-ML. In2022 International Interdisciplinary Humanitarian Conference
for Sustainability (IIHC) 2022 Nov 18 (pp. 1444–1446). IEEE.
22. Friha O, Ferrag MA, Shu L, Maglaras L, Wang X. Internet of things for the future of smart
agriculture: A comprehensive survey of emerging technologies. IEEE/CAA Journal of Automatica
Sinica. 2021;8(4):718–52.
23. Liu J, Jiang W. Optimization of Aquaponics System Efficiency based on Artificial Intelligence
Approach. In2024 IEEE 3rd International Conference on Computing and Machine Intelligence
(ICMI) 2024 Apr 13 (pp. 1–6). IEEE.
24. Jiang W, Liu J. NeuroAqua: Developing an Optimized Artificial Intelligence and Internet of
Things-based Aquaponics System. In2024 IEEE 3rd International Conference on Computing and
Machine Intelligence (ICMI) 2024 Apr 13 (pp. 1–5). IEEE.
25. Nguyen N, Nguyen K, Dinh N, Tran N. Machine learning for the assessment and prediction of
nitrite in the aquaculture water. In2022 International Conference on Multimedia Analysis and
Pattern Recognition (MAPR) 2022 Oct 13 (pp. 1–5). IEEE.
26. Singhal A, Kaur H, Duggal AS, Malik PK, Singh R. Emerging Technologies in the Fish Farming
by Deploying Artificial Intelligence. In2023 4th International Conference on Electronics and
Sustainable Communication Systems (ICESC) 2023 Jul 6 (pp. 1666–1669). IEEE.
27. Venkatraman M, Surendran R. Industrial 5.0 Aquaponics System using machine learning
techniques. In2023 7th International Conference on Electronics, Communication and Aerospace
Technology (ICECA) 2023 Nov 22 (pp. 835–842). IEEE.
28. Hughes JA, Hannah W, Kikkert P, MacKenzie B, Ashlock W, Houghten S, Ashlock D, Stoodley
M, Dubé M, Brown R, Saunders A. We Are Not Pontius Pilate: Acknowledging Ethics and Policy.
In2020 IEEE Symposium Series on Computational Intelligence (SSCI) 2020 Dec 1 (pp. 2975–
2984). IEEE.
29. Wang W, Jia Y, Cai K, Yu W. An aquaponics system design for computational intelligence
teaching. IEEE Access. 2020 Feb 28;8:42364–71.
30. Li Y, Das R, Duong C, Lim TT, Haithcoat T, Ersoy I, Shyu CR. Automated detection of poultry
farms from aerial images for actionable ai system toward biosecurity applications. In2023 IEEE
Applied Imagery Pattern Recognition Workshop (AIPR) 2023 Sep 27 (pp. 1–4). IEEE.
31. Ali M, Imran M, Baig MS, Shah A, Ullah SS, Alroobaea R, Iqbal J. Intelligent Control Shed Poultry
Farm System Incorporating With Machine Learning. IEEE Access. 2024 Apr 22.
32. Feraldi R, Enriko IK. Machine learning model for temperature and humidity automatic control in
smart poultry farm. In2022 International Conference on Advanced Creative Networks and
Intelligent Systems (ICACNIS) 2022 Nov 23 (pp. 1–5). IEEE.

Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 14/04/2025
Accepted 07/04/2025
Published 10/05/2025
Publication Time 26 Days
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