Cross-Layer Reliability Analysis and Edge-Adaptive Multi-Objective Optimization Strategies for Network-Physical Modeling in Intelligent Agriculture CPS Management

Growing food demand and climate stresses drive smart agriculture implementation, but existing Cyber-Physical Systems (CPS) lack dependable cross-layer integration and real-time flexibility, limiting performance in dynamic environments. This protocol aims to provide a cross-layer cyber-physical modeling and optimization strategy for intelligent greenhouse agriculture. It demonstrates potential applicability for enhancing the reliability and adaptability of agricultural Cyber-Physical Systems. The approach integrates a physical layer with the Soil-Plant-Atmosphere Continuum model and Ensemble Kalman Filter (EnKF) calibration for accurate soil moisture prediction. It includes a network layer employing multi-protocol fusion with Stochastic Petri Net modeling to evaluate communication reliability. A control layer builds on a stochastic hybrid system to coordinate joint decision-making. Reliability is further assessed through a functional-temporal-ecological indicator framework, while optimization combines multi-objective reinforcement learning with safety constraints and Bayesian meta-learning to enable rapid adaptation during crop switching. An edge-intelligent deployment ensures robust control during communication interruptions. Results from greenhouse tomato cultivation in Shouguang, China, show reproducible and stable performance in yield prediction, water use efficiency, and control latency under challenging conditions. This methodology provides a practical and replicable workflow for implementing adaptive and reliable agricultural Cyber-Physical Systems.