AI-Driven Nano-Informatics: Methodology and Agricultural Applications

Climate change poses unprecedented challenges to global agricultural systems, threatening food security, ecosystem stability and rural livelihoods. Climate-smart agriculture (CSA) has emerged as an integrative framework to address these challenges by simultaneously enhancing agricultural productivity, strengthening resilience to climate variability and reducing or mitigating greenhouse-gas emissions. In recent years, the convergence of artificial intelligence (AI) and nanotechnology has accelerated the transition towards next-generation CSA by enabling data-driven, precise and resource-efficient farming systems. This chapter examines the conceptual pathways and sustainability implications of AI–nano-enabled climate-smart agriculture. Complementarily, agricultural nanotechnology contributes material-level precision via nano-fertilizers, nano-pesticides, nano-sensors, seed nano-priming and soil and environmental remediation technologies, improving nutrient-use efficiency, crop protection and stress tolerance while minimizing ecological losses. Emphasis is placed on AI–nano integration mechanisms, where nano-sensor networks generate continuous field-level data that are processed through AI models to optimize input application, reduce risk and improve yield stability under climate stress. The chapter further evaluates the behaviour and fate of engineered nanomaterials in soil–plant–environment systems, addressing controlled-release dynamics, plant uptake pathways and sustainability considerations. Overall, the chapter concludes that AI–nano-enabled climate-smart agriculture represents a transformative and scalable pathway toward sustainable food systems. Its successful implementation will depend on interdisciplinary research, robust governance frameworks, life-cycle assessments, capacity building and inclusive policy support to ensure that technological advances translate into equitable, safe and climate-resilient agricultural development.