Climate variability, scarcity of resources, and sustainable agricultural production under the global demand in modern agriculture, including food security. There is no denying the fact that Artificial Intelligence (AI) and Machine Learning (ML) are proving to be significant solutions to these critical issues. This paper is an extensive study on the latest research in the field of Artificial Intelligence (AI) and Machine Learning (ML) techniques or systems used for farming, exclusively emphasising their applications within agriculture context related to agriculture yield prediction, soil health assessment, and resource management and intelligent decision support systems. This study provides a comprehensive literature search that systematically collates and reviews the current knowledge and understanding of the potential for predictive analytics, Internet of Things (IoT), remote sensing, geographic information systems (GIS), and cloud-based technology to help optimize yields, crop forecasting, and sustainability. The review revealed that AI techniques are effective tools for improving yield predictions and monitoring soil health (including nutrient levels, moisture content), for identifying pest and disease issues, and for documenting good practice in the use of water, fertilizer, and pesticide. Furthermore, it addresses the significance of implementing Smart Agriculture based on smart irrigation systems, autonomous machines, and real-time farm monitoring based on using AI-powered automation. Yet on the ground, there are several important challenges to address before the technology can reach its potential, and these will be the determining factors in its future: data quality and interoperability, poor digital infrastructure, high application costs, technical skill, and data privacy and deployment issues. The paper also captures future research directions, such as multimodal data integration, climate-resilient predictive model development, and collaborative models between researchers, policymakers, technology developers, and farmers. On the whole, the paper reveals how AI and ML technologies have the ability to enable an intelligent agriculture ecosystem through which better crop production and resource management can be achieved, and environmental sustainability can also be ensured. These ongoing processes will facilitate the development of resilient, eco-friendly, as well as sustainable food production systems to live up to the future demand of global food security.
Shigan, M. & Arora, P. (2026). Integrating Artificial Intelligence and Machine Learning in Smart Agriculture: A Review. International Journal of Innovations & Research Analysis, 06(03(II)), 75–84. https://doi.org/10.62823/IJIRA/06.3(II).9502
- Jeong, J., Resop, J. P., Mueller, N. D., Fleisher, D. H., Yun, K., Butler, E. E., … & Ahl, D. E. (2016). Random forests for global and regional crop yield predictions. PLOS ONE
- Khaki, S., & Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science
- Kussul, N., Lavreniuk, M., Skakun, S., &Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters
- Mohanty, S. P., Hughes, D. P., &Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science
- “Machine Learning in Agriculture: A Review” (2018). Sensors (MDPI)
- “Crop yield prediction in agriculture: A comprehensive review of AI methods” (2024). PMC / systematic review
- “Revolutionizing agriculture: A comprehensive review on artificial intelligence and precision farming” (2025). PMC
- “Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends” (2022). Applied Sciences / MDPI
- “Implementation of artificial intelligence in agriculture for optimization of farming operations” (2020). Journal / SciDirect
- “AI in Current and Future Agriculture: An Introductory Overview” (2023). KI (Springer)
- “Will artificial intelligence and machine learning change agriculture?” (2023). Agricultural Journal
- “Artificial Intelligence in Precision Agriculture: A Review” (2025). Journal Kejuruteraan
- “Artificial intelligence and its applications in agriculture: A review” (2025). Environment Conservation Journal
- “Artificial Intelligence Tools for the Agriculture Value Chain” (2023). Electronics / MDPI
- “Improving crop production using an agro deep learning framework in precision agriculture” (2024). BMC Bioinformatics
- Naam? (if used) “AI can empower agriculture for global food security” (recent). PMC
- “An interdisciplinary approach to artificial intelligence in agriculture” (2023). Journal (Taylor & Francis)
- “The IoT and AI in Agriculture: The Time Is Now A Systematic Review” (2025). Sensors / MDPI
- “Interpretability and accessibility of machine learning in selected food processing, agriculture and health applications” (2022). arXiv
- “AI in Agriculture: A Survey of Deep Learning Techniques for Crops, Fisheries and Livestock” (2025). arXiv
- “Label Efficient Learning in Agriculture: A Comprehensive Review” (2023). arXiv
- “AgroXAI: Explainable AI Driven Crop Recommendation System for Agriculture 4.0” (2024). arXiv
- “Artificial Intelligence in Agriculture: A Review of Transformative Applications” (2025). Preprints
- “Perspectives on Artificial Intelligence in Agriculture” (2023). Agriculture Journal
- “An interdisciplinary approach to artificial intelligence in agriculture” (2023). Taylor & Francis
- Vishnoi, H., et al. (2021). IoT based soil sensors for real time monitoring (paper)
- Mishra, D., et al. (2020). Geospatial and spectral data integration for soil health mapping
- Chlingaryan, A., Sukkarieh, S., & Whelan, B. (2018). Machine learning approaches for crop yield prediction and soil parameter estimation. Computers and Electronics in Agriculture
- Dutta, A., et al. (2020). Decision support system for optimized irrigation and fertilization scheduling
- Kuhn, N. J., Banerjee, A., Hendrix, P. F., & Bosta, E. (2020). Predictive models for soil erosion and degradation patterns
- FAO. (2017). Status of the world’s soil resources – Main report
- Konikow, L. F., & Kendy, E. (2005). Groundwater depletion: A global problem
- Kleinman, P. J. A., et al. (2011). Agricultural nonpoint source nitrogen: A review
- Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., & Polasky, S. (2002). Agricultural sustainability and intensive production practices
- Godfray, H. C. J., et al. (2010). Food security: the challenge of feeding 9 billion
- Wolfert, S., Ge, L., Verdouw, C., &Bogaardt, M. J. (2017). Big data in smart farming
- Ray, P. P. (2017). A survey on Internet of Things architectures
- Acikgoz, I. (2021). Sensing technologies in soil monitoring
- Loganathan, T., et al. (2021). Soil moisture sensor networks
- Deng, M., et al. (2022). Hyperspectral imaging for soil nutrient estimation
- He, Y., et al. (2022). Remote sensing of soil organic carbon
- Deng, W., et al. (2023). Data fusion models for soil parameter prediction
- Fahad, S., et al. (2021). Smart irrigation systems using AI decision models
- Tian, H., et al. (2021). Predictive modeling for evapotranspiration and water demand
- Gutiérrez, J., et al. (2014). Smart irrigation system and site specific nutrient management
- Patrício, D., & Rieder, R. (2018). AI based recommendation systems for pesticide use
- Bac, C. W., van Asselt, K., & Hemming, J. (2014). Harvesting robots for high-value crops
- González Andújar, J., & Fernández Quintanilla, C. (2019). Scheduling and logistics in farm operations
- Benke, K., & Tomkins, B. (2017). Energy optimization in controlled environment agriculture
- Arif, M., et al. (2013). ANN based soil moisture detection and smart irrigation.
