Artificial Intelligence, Remote Sensing and Digital Twins in Precision Agriculture: Emerging Tools for Climate-Resilient Crop Production

Global agriculture faces unprecedented and compounding pressures arising from accelerating climate change, a growing human population, and the progressive degradation of the natural resource base upon which food production depends. Precision agriculture has emerged as a paradigm that leverages advanced technologies to improve the efficiency, sustainability, and resilience of crop production systems. This review examines the convergence of three transformative technological domains — artificial intelligence (AI), remote sensing, and digital twins — and their collective application in climate-resilient crop production. Drawing on peer-reviewed literature published predominantly between 2005 and 2026, the review evaluates how machine learning and deep learning algorithms facilitate crop monitoring, yield prediction, and disease detection; how satellite and unmanned aerial vehicle (UAV)-based remote sensing provides spatially explicit, high-resolution data on crop biophysical variables; and how digital twin frameworks integrate real-time sensor data, simulation models, and AI analytics to create dynamic virtual replicas of agricultural systems. The review further analyses synergistic applications of these tools in addressing key climate-resilience challenges, including drought stress management, pest and disease surveillance, and yield optimisation under variable climatic conditions. Despite significant progress, substantial challenges remain, encompassing data interoperability, computational demands, and socio-economic barriers to technology adoption — particularly in low-income agricultural economies. The review concludes that the integrated deployment of AI, remote sensing, and digital twins holds transformative potential for climate-smart agriculture, but realising this potential requires concerted investment in rural digital infrastructure, open-data frameworks, and inclusive capacity-building initiatives.

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Publication Details

Journal
International Journal of Environment and Climate Change
Published
2026-04-15
DOI
https://doi.org/10.9734/ijecc/2026/v16i45410
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
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article

Artificial Intelligence, Remote Sensing and Digital Twins in Precision Agriculture: Emerging Tools for Climate-Resilient Crop Production

Lalit Kumar Sanodiya, Shivabasappa Kandkur, Kagita Navya, Kerobim Lakra et al.
International Journal of Environment and Climate Change
Smart Agriculture and AI
article

Artificial Intelligence, Remote Sensing and Digital Twins in Precision Agriculture: Emerging Tools for Climate-Resilient Crop Production

Lalit Kumar Sanodiya, Shivabasappa Kandkur, Kagita Navya, Kerobim Lakra, Shivaji Kallappa Duradundi, Moinuddin
article en

Abstract

Global agriculture faces unprecedented and compounding pressures arising from accelerating climate change, a growing human population, and the progressive degradation of the natural resource base upon which food production depends. Precision agriculture has emerged as a paradigm that leverages advanced technologies to improve the efficiency, sustainability, and resilience of crop production systems. This review examines the convergence of three transformative technological domains — artificial intelligence (AI), remote sensing, and digital twins — and their collective application in climate-resilient crop production. Drawing on peer-reviewed literature published predominantly between 2005 and 2026, the review evaluates how machine learning and deep learning algorithms facilitate crop monitoring, yield prediction, and disease detection; how satellite and unmanned aerial vehicle (UAV)-based remote sensing provides spatially explicit, high-resolution data on crop biophysical variables; and how digital twin frameworks integrate real-time sensor data, simulation models, and AI analytics to create dynamic virtual replicas of agricultural systems. The review further analyses synergistic applications of these tools in addressing key climate-resilience challenges, including drought stress management, pest and disease surveillance, and yield optimisation under variable climatic conditions. Despite significant progress, substantial challenges remain, encompassing data interoperability, computational demands, and socio-economic barriers to technology adoption — particularly in low-income agricultural economies. The review concludes that the integrated deployment of AI, remote sensing, and digital twins holds transformative potential for climate-smart agriculture, but realising this potential requires concerted investment in rural digital infrastructure, open-data frameworks, and inclusive capacity-building initiatives.

International Journal of Environment and Climate ChangeVol. 16(4)
Zero hunger
Openalex Percentile: Top 24%
Smart Agriculture and AI
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