Machine Learning-Based Morel Mushroom Environmental Monitoring System

Abstract

As living standards improve, the demand for edible fungi increases, making their efficient cultivation crucial for food supply and agricultural productivity. Traditional manual monitoring has limited accuracy, but the introduction of IoT and embedded technology enhances the intelligence and precision of agricultural monitoring, saving both labor and resources. This study designs a machine learning-based environmental monitoring and prediction system for edible fungi, utilizing low-power ZigBee networks and Kalman filtering to improve data accuracy. The system ensures real-time data transmission via the MQTT protocol and enhances security and scalability through cloud storage. By comparing Transformer, LSTM, and LSTM-Attention models, it was found that the Transformer model performed best in predicting environmental parameters, enabling proactive regulation, boosting yield and quality, and promoting the development of intelligent agriculture.