IoT-Enabled Precision Epigenomics for Predictive Monitoring and Intelligent Process Control in Climate-Resilient Agriculture

Abstract

Introduction: Climate-resilient agriculture requires process-monitoring systems capable of detecting biological stress before visible crop damage occurs. This study proposes an IoT-enabled precision epigenomics framework that links real-time environmental telemetry with plant molecular-response modelling to support predictive, site-specific agricultural control.

Methods: A four-layer architecture was designed comprising field sensors, edge intelligence, secure data logging, and cloud analytics. Soil moisture, salinity, pH, temperature, water quality, volatile compounds, and remote-sensing indicators are collected as multivariate time-series inputs. A hybrid convolutional neural network–long short-term memory model combines spatial genomic and epigenomic features with temporal environmental histories. The convolutional component extracts sequence- and chromatin-associated motifs, while the long short-term memory component models cumulative drought, heat, salinity, and contaminant exposure. Their fused representation generates probabilities of stress-associated epigenetic changes. Edge-control rules translate model outputs into actions including targeted irrigation, localized remediation, robotic intervention, and food-safety alerts.

Results: The resulting framework integrates environmental monitoring, machine-learning inference, and closed-loop agricultural control within a unified process architecture. It supports early identification of transitions from adaptive stress priming to probable cellular damage while enabling traceability of persistent contaminants, including per- and polyfluoroalkyl substances and microplastics, across soil–water–crop pathways. The architecture also provides a basis for explainable risk dashboards and auditable decision records for agronomists, food-safety authorities, and occupational-health personnel.

Conclusion: IoT-enabled precision epigenomics provides a process-control pathway from reactive crop observation to predictive intervention. Future field validation should quantify prediction accuracy, control efficiency, resource savings, and cross-crop generalizability.