AI-Integrated Smart Farming Solutions for Crop Enhancement

  • Authors

    • Carl Adam Petri Professor of Computer Science, University of Hamburg, Germany Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2025PII7C7F

    Published 08-04-2025

  • Artificial Intelligence, Smart Farming, Precision Agriculture, Crop Enhancement, Machine Learning, Deep Learning, Internet of Things (IoT), Precision Irrigation, Computer Vision, Agricultural Automation

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    C. A. Petri, “AI-Integrated Smart Farming Solutions for Crop Enhancement”, ijmiet, vol. 8, no. 2, pp. 01–15, Aug. 2025, doi: 10.67228/30715628/IJMIET-2025PII7C7F.

  • Abstract

    With the speed of AI development, the agriculture sector has been revolutionized, with intelligent, data-driven farming practices enhancing productivity and sustainability while optimizing the use of resources. Smart farming with AI technology involves the integration of machine learning, deep learning, computer vision, Internet of Things (IoT), unmanned aerial vehicles (UAVs), remote sensing, and cloud computing, all working together to track crop conditions, forecast weather, manage water usage, identify plant diseases, and improve agricultural decision-making. In traditional farming, making important observations and decisions is usually done by hand and based on general knowledge, which results in waste of resources and poor crop results. AI-driven precision agriculture, on the other hand, enables farmers to track crop health in real time and make predictions that help them take proactive measures to optimise quality and reduce expenses in their operations.Precision agriculture, on the other hand, powered by AI allows farmers to monitor and predict crop health, enabling them to take proactive steps to maximise crop quality while minimising operational costs. This study reviewed the state-of-the-art smart farming solutions integrated with AI, specifically aimed at crop improvement. The paper outlines the history of smart agriculture, examines the latest AI-driven innovations in agriculture, offers insights into ongoing research and development challenges, and recommends potential future research opportunities. In addition, it highlights the importance of data analytics, sensor technologies, and autonomous agricultural systems in the context of sustainable food production in a world of growing population and climate change. Research objectives are to develop a conceptual framework based on multiple AI technologies that can be combined in a single smart farming ecosystem to boost productivity, minimize environmental impacts, increase decision making accuracy and to support sustainable agricultural development. The results have shown that smart farming with the use of artificial intelligence is a paradigm.

  • References

    [1] J. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, "Big Data in Smart Farming—A Review," Agricultural Systems, vol. 153, pp. 69–80, May 2017.

    [2] V. Kamilaris and F. X. Prenafeta-Boldú, "Deep Learning in Agriculture: A Survey," Computers and Electronics in Agriculture, vol. 147, pp. 70–90, Apr. 2018.

    [3] A. Khanna and S. Kaur, "Evolution of Internet of Things (IoT) and Its Significant Impact in the Field of Precision Agriculture," Computers and Electronics in Agriculture, vol. 157, pp. 218–231, Feb. 2019.

    [4] L. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, "Machine Learning in Agriculture: A Review," Sensors, vol. 18, no. 8, pp. 1–29, 2018.

    [5] S. R. Dubey, S. K. Singh, and B. B. Chaudhuri, "Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark," Neurocomputing, vol. 503, pp.

    [6] N. Talaviya, D. Shah, N. Patel, H. Yagnik, and M. Shah, "Implementation of Artificial Intelligence in Agriculture for Optimisation of Irrigation and Application of Pesticides and Herbicides," Artificial Intelligence in Agriculture, vol. 4, pp. 58–73, 2020.

    [7] S. Maheshwari, A. Jain, and P. Pachauri, "Cloud Computing Based Smart Agriculture Framework Using Internet of Things," Procedia Computer Science, vol. 171, pp. 1908–1917, 2020.

    [8] J. Zhang, Y. Huang, R. Pu, P. Gonzalez-Moreno, and L. Yuan, "Monitoring Crop Growth with UAV-Based Remote Sensing: A Review," Remote Sensing, vol. 11, no. 20, pp. 1–28, 2019.

    [9] F. Ferrández-Pastor, J. M. García-Chamizo, M. Nieto-Hidalgo, and J. Mora-Pascual, "Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture," Sensors, vol. 16, no. 7, pp. 1–20, 2016.

    [10] B. Basso and J. Antle, "Digital Agriculture to Design Sustainable Agricultural Systems," Nature Sustainability, vol. 3, no. 4, pp. 254–256, 2020.

    [11] S. Fountas, M. Carli, C. G. Sørensen, Z. Tsiropoulos, B. Cavalaris, T. Vatsanidou, M. Liakos, and D. Bochtis, "Farm Management Information Systems: Current Situation and Future Perspectives," Computers and Electronics in Agriculture, vol. 115, pp. 40–50, 2015.

    [12] M. Javaid, A. Haleem, R. P. Singh, and R. Suman, "Artificial Intelligence Applications for Industry 4.0: A Literature-Based Study," Journal of Industrial Integration and Management, vol. 7, no. 1, pp. 83–111, 2022.

    [13] C. Verdouw, J. Wolfert, B. Tekinerdogan, M. Beulens, and A. J. M. Verloop, "Digital Twins in Smart Farming," Agricultural Systems, vol. 189, Art. no. 103046, 2021.

    [14] M. Shamshiri, C. Weltzien, I. A. Hameed, I. J. Yule, T. E. Grift, S. K. Balasundram, L. Pitonakova, D. Ahmad, and G. Chowdhary, "Research and Development in Agricultural Robotics: A Perspective of Digital Farming," International Journal of Agricultural and Biological Engineering, vol. 11, no. 4, pp. 1–14, 2018.

    [15] R. K. Gupta, A. Patel, and S. Kumari, "Artificial Intelligence and Internet of Things for Smart Agriculture: Recent Advances and Future Challenges," IEEE Access, vol. 10, pp. 115892–115917, 2022.

    [16] Taluri, R. (2024). A Cloud-Native Reference Architecture for Data Engineering, Generative AI, and Decision Intelligence Using AWS and Amazon Bedrock. International Journal of AI, BigData, Computational and Management Studies, 5(1), 218-227. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I1P122

    [17] Veershetty, G. (2023). Risk-adaptive transition and transformation (RATT): A predictive governance framework for SAP cloud migration programs. International Journal of Leading Research Publication, 4(12). https://doi.org/10.70528/IJLRP.v4.i12.2170

    [18] Gajula, S. (2023). A review of anomaly identification in finance frauds using machine learning system. International Journal of Current Engineering and Technology, 13(6), 568–575. https://ijcet.evegenis.org/index.php/ijcet/article/view/820

    [19] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

    [20] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management

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