AI-Assisted Nano-Omics Approaches in Agriculture: Opportunities and Challenges

The convergence of artificial intelligence (AI), nanotechnology, and multi-omics has opened transformative pathways in agriculture, enabling precise, predictive, and sustainable food production systems. This chapter explores the emerging landscape of AI-assisted nano-omics approaches, detailing how they collectively enhance plant health monitoring, nutrient use efficiency, stress resilience, and microbiome dynamics. By integrating multi-layered biological data with nanodevices and AI-driven analytics, these technologies allow for real-time decision-making, early disease detection, and optimized input management across diverse agroecosystems. We begin by contextualizing the role of omics (genomics, transcriptomics, proteomics, metabolomics) in modern agriculture, highlighting their synergistic potential when paired with AI for big data processing and nanodevices for targeted delivery and diagnostics. The chapter then delves into technical advances in nano-biosensors, smart fertilizers, and precision phenotyping platforms, demonstrating how AI algorithms elevate the functionality of these tools through pattern recognition and predictive modeling. Despite these innovations, we underscore critical challenges, including data integration complexity, algorithm transparency, nano-toxicity concerns, and infrastructural disparities in low-income regions. To address these, the chapter provides policy and practice recommendations centered on ethical AI governance, biosafety regulation, interdisciplinary training, and inclusive technology access.