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

Summary

This protocol presents a cross-layer cyber-physical modeling and optimization strategy for intelligent greenhouse management, enabling reproducible assessment of reliability and ecological performance.

Abstract

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.

Introduction

The world population is growing rapidly, and resource availability is declining, which is transforming the way agriculture is developed. The conventional models of agriculture, where labor and material input are high, and the reliance on the natural circumstances is strong, cannot offer efficiency and sustainability. Smart agriculture has, in this case, become the transformative approach. It allows achieving a complete field perception, making accurate decisions, and controlling the field intelligently through the combination of the Internet of Things, big data analytics, artificial intelligence, and spatial information systems, which enhances the efficiency of the us....

Protocol

It is noted that no experiments on human beings or vertebrate animals are involved in this protocol. In case of any future studies that will involve human involvement or biological samples, this has to be approved by the concerned institutional review board, and the approval number must be recorded prior to implementation.

1. Site and hardware preparation

NOTE: This step builds a standardized sensor network, which gives precise and synchronized information about the environment to be used in subsequent physical modeling and control.

1. Establish the location of the greenhouse by inputtin....

Results

Soil moisture and SNR have the greatest positive SHapley Additive exPlanations (SHAP) influence on irrigation decisions, according to the SHAP summary plot (Figure 3). High latency drives the policy toward conservative fallback options, demonstrating conformity with the reliability-aware control architecture. Table 4 depicts operational instructions and configuration details for computational modules

Experimental setup

Discussion

The experimental evidence shows that the proposed CPS framework and optimization strategies excel across the three dimensions of reliability, safety, and computational efficiency. Cross-layer coupling modeling successfully overcomes the historical separation between physical and network representations. By embedding SPAC and SPN within a unified SHS framework, the system reduced yield prediction error by 32.7% and shortened delays by 45% under extreme high temperatures. Safety constraints ensured that ecological performa.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Multispectral Camera	MicaSense	RedEdge-MX	Captures canopy reflectance for LAI estimation
NVIDIA Jetson Nano	NVIDIA	945-13450-0000-100	Edge device for local AI inference
Soil Moisture Sensor	Decagon Devices	EC-5	Measures volumetric water content in soil
Weather Station	Campbell Scientific	CR300	Records temperature, humidity, and rainfall