IoT-Enabled Precision Epigenomics and AI-Driven Analytics: A Synergistic Framework for Climate-Resilient Agriculture, Environmental Sustainability, and Public Health Monitoring

The rapid acceleration of the global climate crisis poses an existential threat to food security, environmental hygiene, and global public health. Traditional agricultural frameworks lack the predictive granularity required to withstand hyper-local abiotic fluctuations and systemic eco-toxicity. This paper establishes a novel computational and physical paradigm—IoT-Enabled Precision Epigenomics—operating at the intersection of Agriculture 5.0 and the One Health mandate. We present a hybrid deep learning architecture uniting Convolutional Neural Networks (CNNs) for spatial genomic/epigenomic motif extraction with Long Short-Term Memory (LSTM) Recurrent Neural Networks for temporal environmental stress-memory modeling. By feeding real-time telemetry from Internet of Things (IoT) field sensor matrices into this network, the system decodes and predicts site-specific plant epigenetic modifications (such as DNA methylation and histone acetylation) before physical phenotypic degradation occurs. Furthermore, this intelligence layer is physically coupled with automated robotics and decentralized via blockchain ledgers to secure data integrity. We evaluate this system across three integrated deployment domains: climate-resilient delta agro-ecosystems, environmental contaminant tracing (PFAS and microplastics tracking), and occupational hazard mitigation. Finally, we address critical sociotechnical barriers, including farmers' adaptation behaviors, ethical constraints, and algorithmic governance. Complete programmatic architectures for rendering the methodology models using Python are provided to ensure open-source reproducibility.