Air pollution, particularly fine particulate matter (PM2.5), is a vital yet inadequately researched hazard to Northern Indian crop productivity. Rice residue burning seasons in Punjab and Haryana deliver record PM2.5 levels, with potential impacts not only on human health but also on the physiology and yield of crops. This chapter investigates the processes by which PM2.5 affects rice cultivation and proposes a digital twin (DT) approach to detect, model, and mitigate these effects. A conceptual DT structure is introduced that integrates real-time air quality monitoring, satellite/UAV imagery, and agro-environmental simulation models. Empirical studies from remote sensing and ground-based sensor networks are summarized, showing high levels of pollution exposure in rice farming areas. The chapter addresses the prospects of DTs for augmenting precision agriculture, enlightening policy-making, and encouraging resilient agricultural systems. Lastly, it outlines research and technical innovations required to upscale DTs in the Indo-Gangetic Plains so as to enhance sustainable agriculture under increasing air pollution.