基于边缘智能的自适应农业物联网系统OA

The decision-making of traditional agricultural monitoring and control systems relies on cloud computing,which has significant shortcomings in terms of energy consumption,latency,and sus-tainability.To address the above issues,this study designs an edge-intelligent adaptive environment mo-nitoring and control system based on ESP32+TinyML(Tiny Machine Learning).The system deploys a lightweight TinyML model on the node side for environmental trend prediction and anomaly recognition,which can effectively reduce redundant energy consumption and communication load.The system adopts a"solar energy and supercapacitor"solution for energy optimization design,combining deep sleep and on-demand wake-up.After experimentation,the system has achieved adaptive monitoring,trend prediction of node environmental information,and autonomous regulation.This study provides a feasible path for a low-power,predictive,and sustainable smart agricultural IoT system.