AI and AIoT's Potential to Revolutionize Contemporary Agriculture: A Review

Authors

  • Prof. Neha Patil Assistant Professor Author
  • Prof. Kapil Patil Government Polytechnic, Dhule Author

Keywords:

Keywords: AI, AIoT, Precision Farming, Smart Agriculture, Crop Yield Forecasting, Sustainable Farming.

Abstract

Abstract-Agriculture is encountering escalating difficulties due to climate change, limited water resources, diminishing soil fertility, and rising food demand. Artificial Intelligence (AI) and the Artificial Intelligence of Things (AIoT) are becoming significant technologies for enhancing agricultural productivity and sustainability. This document examines the use of AI for predicting crop yields, analysing soil, implementing smart irrigation, recommending crop fertilizers, forecasting market prices, and managing resources. Through the incorporation of machine learning, IoT devices, drones, satellite imagery, and meteorological information, AI-powered systems facilitate real-time monitoring and data-driven decision-making minimize ecological footprint and promote sustainable agricultural methods. Although there are obstacles concerning expenses, infrastructure, and data handling, AI and AIoT present considerable opportunities to improve food security and revolutionize contemporary agriculture.

References

[1] Neetu Gangwani, “AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency”, McCombs School of Business, USA (IJFMR) E-ISSN: 2582-2160 .

[2] Shashank Karna, Radhika Kotechaa*, Ritesh Kumar Pandeya’ Towards Sustainable Farming: Leveraging AIoT for Precision Water Management and Crop Yield Optimization ‘K. J. Somaiya Institute of Technology, University of Mumbai, Mumbai – 400022,

[3] Farooq, M., Riaz, S., Abid, A., Umer, T., and Zikria, Y. "Role of IoT Technology in Agriculture: A Systematic Literature Review." Electronics 9, no. 2 (2020). https://doi.org/10.3390/electronics9020319.

[4] Open Government Data Platform India. Accessible: https://data.gov.in. Accessed on March 15, 2023.

[5] Kumar, A., et al. (2019). "IoT and AI-Based Water Conservation Strategies." Precision Farming Journal, 10(4), 87-99.

[6] Patel, R., & Singh, M. (2022). "Reinforcement Learning in Smart Agriculture." Computational Agriculture, 20(1), 33-47.

[7] FAO. The Future of Food and Agriculture: Trends and Challenges. Food and Agriculture Organization, Rome.

[8] Liakos, K.G., et al. “Machine Learning in Agriculture: A Review.” Sensors, 2018.

[9] Wolfert, S., et al. “Big Data in Smart Farming.” Agricultural Systems, 2017.

[10] Kamilaris, A., Prenafeta-Boldú, F.X. “Deep Learning in Agriculture.” Computers and Electronics in Agriculture, 2018.

[11] Zhang, C., Kovacs, J.M. “The Application of Small Unmanned Aerial Systems for Precision Agriculture.” Precision Agriculture, 2012.

[12] Sharma, A., et al. “Artificial Intelligence in Agriculture: A Review.” Sustainable Computing, 2023.

[13] United Nations. World Population Prospects 2024.

[14] IEEE Access, Elsevier, Springer, and MDPI articles on AI-driven agriculture (2021–2025).

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Published

2026-06-30

How to Cite

AI and AIoT’s Potential to Revolutionize Contemporary Agriculture: A Review. (2026). International Journal of Advanced Research in Science, Management and Technology, 12(3), 1-6. https://ijarsmt.in/ijarsmt/article/view/52

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