Development and Validation of a Low-Cost IOT-Based Prototype for Smart Mushroom Cultivation System

Abstract - Mushroom cultivation requires precise control over environmental variables like Temperature, Relative Humidity (RH), and CO2 levels. Traditional methods are labor-intensive, prone to error, and often result in inconsistent yields. This paper presents a novel approach to automated mushroom farming using a framework that integrates the Internet of Things (IoT) and Artificial Intelligence (AI). Our proposed system utilizes a network of IoT sensors to continuously monitor critical environmental data within a cultivation chamber. An AI-powered control algorithm then analyses this data to make intelligent, autonomous decisions, such as activating humidifiers, ventilation fans, or cooling systems, to maintain optimal conditions for mushroom growth. The system incorporates machine learning models to predict ideal harvest times and detect diseases early, addressing key challenges in mushroom cultivation. A Convolutional Neural Network (CNN) analyses images captured by an integrated camera system to monitor mushroom growth stages and identify signs of common diseases. This predictive and diagnostic capability allows for proactive intervention, minimizing crop loss and maximizing yield. The results validate the effectiveness of our integrated IoT-AI framework in creating a self-regulating, high-efficiency mushroom farming environment, paving the way for more sustainable and productive agricultural practices. Key Words: Artificial Intelligence; Convolutional Neural Network (CNN); Internet of Things (IoT); Mushroom Cultivation; Precision Agriculture