A review on Artificial Intelligence, Machine Learning and IoT integration in Precision Agriculture

Authors

  • Harshit Mishra Department of Agricultural Economics, College of Agriculture, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya-224229, Uttar Pradesh, India

DOI:

https://doi.org/10.25081/jsa.2025.v9.9497

Keywords:

Agriculture, AI, Efficiency, Innovation, IoT, Sustainability

Abstract

Artificial Intelligence is revolutionizing agriculture by enhancing efficiency and promoting sustainability through advanced technologies. Machine learning and deep learning, including supervised, unsupervised, and reinforcement learning, enable precise crop and soil monitoring, as well as early detection of diseases and pests, leading to improved yield predictions and more effective resource management. Statistical evidence highlights a 30% increase in crop yields through AI-driven data analytics and precision farming techniques. Robotics and automation, such as automated harvesting systems, streamline operations, reducing labour costs by up to 25%. The integration of IoT and sensor networks facilitates smart irrigation systems, optimizing water use by 20-40%. Moreover, AI aids in climate change mitigation, reducing carbon footprints by employing adaptive strategies. Economic analyses reveal a significant return on investment for AI adoption, with emerging markets in agri-tech forecasted to grow by 18% annually. Despite challenges like high initial costs and data privacy concerns, the potential of AI to transform agriculture is immense, promising more sustainable and productive farming practices.

Downloads

Download data is not yet available.

References

Ahuja, S., & Mehra, P. (2023). Sustainable Artificial Intelligence Solutions for Agricultural Efficiency and Carbon Footprint Reduction in India. In O. Catalpas (Ed.), Agricultural Economics and Agri-Food Business London, UK: Intech Open Limited. https://doi.org/10.5772/intechopen.112996

Alam, M. A., Ahad, A., Zafar, S., & Tripathi, G. (2020). A neoteric smart and sustainable farming environment incorporating blockchain‐based artificial intelligence approach. In G. Shrivastava, D.-N. Le & K. Sharma (Eds.), Cryptocurrencies and Blockchain Technology Applications (pp. 197-213) Massachusetts, US: Scrivener Publishing LLC. https://doi.org/10.1002/9781119621201.ch11

Alreshidi, E. (2019). Smart sustainable agriculture (SSA) solution underpinned by internet of things (IoT) and artificial intelligence (AI). International Journal of Advanced Computer Science and Applications, 10(5), 93-102. https://doi.org/10.14569/IJACSA.2019.0100513

Balaska, V., Adamidou, Z., Vryzas, Z., & Gasteratos, A. (2023). Sustainable crop protection via robotics and artificial intelligence solutions. Machines, 11(8), 774. https://doi.org/10.3390/machines11080774

Dara, R., Fard, S. M. H., & Kaur, J. (2022). Recommendations for ethical and responsible use of artificial intelligence in digital agriculture. Frontiers in Artificial Intelligence, 5, 884192. https://doi.org/10.3389/frai.2022.884192

Dharmaraj, V., & Vijayanand, C. (2018). Artificial intelligence (AI) in agriculture. International Journal of Current Microbiology and Applied Sciences, 7(12), 2122-2128. https://doi.org/10.20546/ijcmas.2018.712.241

Goralski, M. A., & Tan, T. K. (2020). Artificial intelligence and sustainable development. The International Journal of Management Education, 18(1), 100330. https://doi.org/10.1016/j.ijme.2019.100330

Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. https://doi.org/10.1016/j.aac.2022.10.001

Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12. https://doi.org/10.1016/j.aiia.2019.05.004

Khandelwal, P. M., & Chavhan, H. (2019). Artificial intelligence in agriculture: An emerging era of research. ResearchGate Publication, 1(1), 56-60.

Lakshmi, V., & Corbett, J. (2020). How artificial intelligence improves agricultural productivity and sustainability: A global thematic analysis. Proceedings of the 53rd Hawaii International Conference on System Sciences (pp. 5202-5211). ScholarSpace. https://doi.org/10.24251/HICSS.2020.639

Maraveas, C. (2022). Incorporating artificial intelligence technology in smart greenhouses: Current State of the Art. Applied Sciences, 13(1), 14. https://doi.org/10.3390/app13010014

Martos, V., Ahmad, A., Cartujo, P., & Ordoñez, J. (2021). Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Applied Sciences, 11(13), 5911. https://doi.org/10.3390/app11135911

Mishra, H., & Mishra, D. (2023). Artificial intelligence and machine learning in agriculture: Transforming farming systems. In Research Trends in Agriculture Science (Vol. 1, pp. 1-16) Maharashtra, India: Bhumi Publishing. https://doi.org/10.5281/zenodo.15157536

Mishra, H., & Mishra, D. (2024). Economic Evaluation of UAV-Based Soil Sampling Approaches. In S. S. Chouhan, U. P. Singh & S. Jain (Eds.), Applications of Computer Vision and Drone Technology in Agriculture 4.0 (pp. 271-291) Singapore: Springer. https://doi.org/10.1007/978-981-99-8684-2_15

Mishra, H., Nishad, D. C., Tiwari, A. K., Singh, R., & Mishra, D. (2024). Market Forecasting and Price Analysis with Artificial Intelligence in Agriculture. In S. A. Waske, P. K. Thakar, N. Bhavya & A. Singh (Eds.), Advances in Agriculture Sciences (Vol. 4, pp. 98-117) Maharashtra, India: Bhumi Publishing.

Nishad, D. C., Mishra, H., Tiwari, A. K., & Mishra, D. (2024). Post-Harvest Management: Enhancing Food Security and Sustainability. In G. Singh, P. Singh, A. K. Pandey & S. S. Rokade (Eds.), Advances in Agriculture Sciences (Vol. 2, pp. 136-152) Maharashtra, India: Bhumi Publishing.

Nishad, D. C., Mishra, H., Tiwari, A. K., Singh, R., & Mishra, D. (2024). Economic Realities and Sociocultural Dynamics in Modern Agriculture. In S. A. Waske, P. K. Thakar, N. Bhavya & A. Singh (Eds.), Advances in Agriculture Sciences (Vol. 4, pp. 66-82). Maharashtra, India: Bhumi Publishing.

Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104. https://doi.org/10.1016/j.ijinfomgt.2020.102104

Sachithra, V., & Subhashini, L. D. C. S. (2023). How artificial intelligence uses to achieve the agriculture sustainability: Systematic review. Artificial Intelligence in Agriculture, 8, 46-59. https://doi.org/10.1016/j.aiia.2023.04.002

Salehi, F. (2024). The Role of Artificial Intelligence in Revolutionizing the Agriculture Industry in Canada. Asian Journal of Research and Review in Agriculture, 6(1), 70-78.

Sarfraz, S., Ali, F., Hameed, A., Ahmad, Z., & Riaz, K. (2023). Sustainable agriculture through technological innovations. In C. S. Prakash, S. Fiaz, M. A. Nadeem, F. S. Baloch & A. Qayyum (Eds.), Sustainable agriculture in the era of the OMICs revolution (pp. 223-239). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-031-15568-0_10

Shaikh, F. K., Memon, M. A., Mahoto, N. A., Zeadally, S., & Nebhen, J. (2021). Artificial intelligence best practices in smart agriculture. IEEE Micro, 42, 17-24. https://doi.org/10.1109/MM.2021.3121279

Smith, M. J. (2018). Getting value from artificial intelligence in agriculture. Animal Production Science, 60, 46-54. https://doi.org/10.1071/AN18522

Sood, A., Bhardwaj, A. K., & Sharma, R. K. (2022). Towards sustainable agriculture: key determinants of adopting artificial intelligence in agriculture. Journal of Decision Systems, 33(4), 833-877. https://doi.org/10.1080/12460125.2022.2154419

Sood, A., Sharma, R. K., & Bhardwaj, A. K. (2022). Artificial intelligence research in agriculture: a review. Online Information Review, 46(6), 1054-1075. https://doi.org/10.1108/OIR-10-2020-0448

Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58-73. https://doi.org/10.1016/j.aiia.2020.04.002

Taneja, A., Nair, G., Joshi, M., Sharma, S., Sharma, S., Jambrak, A. R., Roselló-Soto, E., Barba, F. J., Castagnini, J. M., Leksawasdi, N., & Phimolsiripol, Y. (2023). Artificial intelligence: Implications for the agri-food sector. Agronomy, 13(5), 1397. https://doi.org/10.3390/agronomy13051397

Tiwari, A. K., Mishra, H., Nishad, D. C., & Pandey, A. (2023). Sustainable water management in agriculture: irrigation techniques and water conservation. In M. Baruah, A. Bhogave, A. B. Jadhao & R. Mallik (Eds.), Research Trends in Agriculture Science (Vol. 2, pp. 53-66) Maharashtra, India: Bhumi Publishing.

Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I. (2021). Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants, 7, 864-876. https://doi.org/10.1038/s41477-021-00946-6

Published

21-07-2025

How to Cite

Mishra, H. (2025). A review on Artificial Intelligence, Machine Learning and IoT integration in Precision Agriculture. Journal of Scientific Agriculture, 9, 101–109. https://doi.org/10.25081/jsa.2025.v9.9497

Issue

Section

Articles