From Nano-Design to Field Deployment: An AI-Enabled Framework for Precision Agriculture

Abstract

Functional nanotechnologies can improve agrochemical delivery, nutrient-use efficiency, and plant stress monitoring, yet their performance often varies under heterogeneous field conditions. This perspective proposes a three-stage, AI-enabled framework integrating predictive nanoformulation design, adaptive field deployment, and feedback-driven optimization. AI integrates material properties with soil, plant, climatic, and field-performance data while supporting ecological risk assessment and compatibility with precision application systems. Key challenges include limited field-scale data sets, model transferability, sensing reliability, standardization, regulatory uncertainty, and economic feasibility. Integrating material engineering with data-driven decision-making may enable more precise, adaptive, and environmentally responsible deployment of agricultural nanotechnologies.