AI-Driven Nano-Bio inoculants in Phytopathogenomics and Disease Control: Integrating Nanotechnology, Functional Genomics, and Smart Sustainable Agriculture

Abstract

Technological advancements in plant disease management have been rapidly gaining momentum, with the rise in response of phytopathogens to conventional agrochemicals and the increasing need for sustainable crop protection. The concept of artificial intelligence (AI)-driven nano-bioinoculants is a groundbreaking interdisciplinary innovation aimed at enhancing the ability to identify phytopathogens, predict diseases and resistances, and cultivate plants to enhance resistance. This review highlights the applications of Nano-bioinoculants in comparative genomics, pathogen evolution, molecular pathogenesis and host–pathogen interaction in disease control and phytopathogenomics. The advent of AI-driven genomic and bioinformatic tools has revolutionized our understanding of the diversity and virulence mechanisms of phytopathogens, and their mechanisms of evolutionary adaptation and transformation; which led to precision targeted disease management approaches. The review also covers the molecular mechanisms of pathogenicity and virulence such as signal transduction pathways, pathogen-induced host responses and biology of pathogen effectors as well as role of nano-enabled beneficial microbes in inhibiting plant diseases and strengthening plant immunity. The application of functional genomics and advances in population genetics using genome-editing technologies like CRISPR/Cas systems for engineering disease-resistant crops and discovering new molecular targets for phytopathogen control are analyzed. Furthermore, AI-powered computational modelling, machine learning algorithms, and bioinformatics platforms are being increasingly used to monitor diseases and perform predictive epidemiology, among other precision agriculture uses. Special focus on improving the efficiency, stability, and selective action of bioinoculants in different environmental conditions through plant–microbe interactions, host specificity studies, and nano-mediated delivery systems. The application of AI, nanotechnology, and phytopathogenomics offers new potential for smart Sustainable Agriculture through the significant decrease in dependence on chemical pesticides, decrease in environmental hazard, and decrease in food security loss. Last but not least, this review puts forward some of the existing restrictions, risk matters related to the biosafety of AI nano-bioinoculants, compliance issues, and future prospects of the application of AI in bioinoculants. AI-driven molecular plant pathology and nanobiotechnology are poised to revolutionize the concept, design, and implementation of disease management systems for the future, and play a major role in assisting in the practice of resilient and climate-smart agriculture. Keywords: Artificial intelligence (AI), nano-bioinoculants, phytopathogenomics, comparative genomics, nanotechnology in agriculture, computational biology, bioinformatics, genome editing, CRISPR/Cas systems, sustainable agriculture, plant pathology, smart farming.