Polyaniline nanocomposite matrices in next-generation agri-sensing: A material-to-algorithm review of interfacial mechanisms, metaheuristic optimization, and agentic AI pathways

Polyaniline (PANI)-based materials continue to bridge materials science across agricultural electronics due to their inherent redox behavior, structural tunability, and ease of chemical synthesis. However, translating lab-scale polymer performance into resilient, real-world field diagnostics requires overcoming multi-interfacial degradation, thermal and humidity cross-sensitivity, and unoptimized material combinatorics. Unlike prior reviews focusing on narrow aspects of conducting polymers, this cross-cutting work synthesizes chemical oxidative, electrochemical, and green synthesis routes for PANI, critically evaluating how acid dopants and nanomaterial fillers, like MXenes, graphene and metal oxides, govern electrical and mechanical transport properties. Emerging fabrication strategies like inkjet printing, dip-coating, and laser-induced graphene are highlighted for scalable deployment. Moving beyond isolated applications, this review introduces a novel material-to-algorithm decision superstructure designed to match specific agronomic target analytes with optimal multi-layer sensing, substrate, and encapsulation configurations. Crucially, we elucidate the integration of physics-informed artificial intelligence pathways into the sensor lifecycle by separating operations into AI for signal processing and AI for material design optimization. The framework utilizes Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm II to navigate non-linear compositional spaces, while implementing temporal Long Short-Term Memory networks and goal-oriented Agentic AI loops for advanced field signal processing. To ensure field reliability, post-hoc Explainable AI algorithms are introduced to mathematically audit sensor decisions against true biochemical interactions, mitigating black-box shortcuts of traditional AI. Finally, we analyze systemic edge-computing and material bottlenecks under field stresses, providing an integrated engineering roadmap to exploit ternary PANI nanocomposite synergies for autonomous, self-correcting, and scientifically validated next-generation smart agricultural sensors.