International Journal of Environment and Climate Change

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.