Artificial Intelligence and Digital Technologies for Smart Farming and Precision Agriculture
- 1Department of Zoology, Indira Gandhi University, Meerpur, Rewari, HR, India
- 2Department of Biotechnology, Noida Institute of Engineering and Technology, Greater Noida, UP, India
- 3Department of Computer Science, UIET, M.D. University, Rohtak, HR, India
Abstract
Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) are transforming precision agriculture by enabling data-driven decision-making, intelligent automation, and sustainable resource management. This review highlights recent advances in AI-enabled smart farming, focusing on applications such as crop yield prediction, disease and pest detection, soil health assessment, weather forecasting, precision irrigation, and livestock monitoring. The integration of Deep Learning (DL), Computer Vision (CV), IoT-enabled sensor networks, unmanned aerial vehicles (UAVs), and edge computing has significantly improved the accurHacy and efficiency of agricultural operations while optimizing the use of water, fertilizers, and pesticides. Emerging technologies, including Generative AI, Large Language Models (LLMs), Vision Transformers (ViTs), Digital Twins, Explainable AI (XAI), and Federated Learning, are further enhancing real-time decision support, predictive analytics, autonomous farming, and climate-resilient agricultural practices. The review also discusses current challenges, including data interoperability, infrastructure limitations, cybersecurity, model transparency, and technology adoption by smallholder farmers. Future smart farming systems are expected to integrate AI, IoT, advanced robotics, satellite and drone-based remote sensing, and intelligent decision-support platforms to improve productivity, sustainability, and food security. Overall, the convergence of AI and digital technologies provides a promising pathway toward resilient, efficient, and environmentally sustainable agriculture.
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BibTeX
@article{tyagi2026artificial,
title = {{Artificial Intelligence and Digital Technologies for Smart Farming and Precision Agriculture}},
author = {Shruti Tyagi and Pankaj Kumar Tyagi and Arvind Kumar and Raj Kumar Yadav},
journal = {Phytoresearch and Technology},
year = {2026},
month = aug,
doi = {10.67443/pt-2026-01010004},
url = {https://doi.org/10.67443/pt-2026-01010004},
note = {CC BY},
}
RIS
TY - JOUR
TI - Artificial Intelligence and Digital Technologies for Smart Farming and Precision Agriculture
AU - Shruti Tyagi
AU - Pankaj Kumar Tyagi
AU - Arvind Kumar
AU - Raj Kumar Yadav
PY - 2026
DA - 2026/08/29
JO - Phytoresearch and Technology
DO - 10.67443/pt-2026-01010004
UR - https://doi.org/10.67443/pt-2026-01010004
AB - Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) are transforming precision agriculture by enabling data-driven decision-making, intelligent automation, and sustainable resource management. This review highlights recent advances in AI-enabled smart farming, focusing on applications such as crop yield prediction, disease and pest detection, soil health assessment, weather forecasting, precision irrigation, and livestock monitoring. The integration of Deep Learning (DL), Computer Vision (CV), IoT-enabled sensor networks, unmanned aerial vehicles (UAVs), and edge computing has significantly improved the accurHacy and efficiency of agricultural operations while optimizing the use of water, fertilizers, and pesticides. Emerging technologies, including Generative AI, Large Language Models (LLMs), Vision Transformers (ViTs), Digital Twins, Explainable AI (XAI), and Federated Learning, are further enhancing real-time decision support, predictive analytics, autonomous farming, and climate-resilient agricultural practices. The review also discusses current challenges, including data interoperability, infrastructure limitations, cybersecurity, model transparency, and technology adoption by smallholder farmers. Future smart farming systems are expected to integrate AI, IoT, advanced robotics, satellite and drone-based remote sensing, and intelligent decision-support platforms to improve productivity, sustainability, and food security. Overall, the convergence of AI and digital technologies provides a promising pathway toward resilient, efficient, and environmentally sustainable agriculture.
SN - 3136-4462
SN - 3136-7992
ER -