A Sustainable IoT-Based Smart System for Mushroom Cultivation Using Fuzzy Logic and Predictive Control

This paper presents the design and implementation of a Sustainable Mushroom House System (SMHS), an IoT-enabled smart farming framework developed to optimize mushroom cultivation using fuzzy logic and predictive control. The primary objective of this system is to create a cost-effective, scalable, and energy-efficient solution for maintaining ideal environmental conditions in mushroom houses with minimal human intervention. The SMHS architecture consists of three main modules: (i) a smart control panel that functions as a centralized gateway, (ii) a sensor system for real-time environmental monitoring, and (iii) a mobile application for remote access and manual overrides. The system integrates sensors (SCD30 and DHT22), actuators (fans and misting systems), and a Raspberry Pi 3B+ microcontroller to manage temperature, humidity, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> levels. Sensor data is analyzed and visualized on the ThingSpeak platform, while control actions are executed through fuzzy logic and predictive analytics. Real-time alerts and manual controls are accessible via the Blynk mobile app. The predictive module enhances decision-making by forecasting environmental changes, ensuring proactive control. Field deployment over four months demonstrated the system's effectiveness: temperature was maintained within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 0.5^{\circ} \mathrm{C}$</tex> and humidity within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 2 \%$</tex>, resulting in a 30% reduction in water usage and a 20% decrease in energy consumption compared to manual systems. These results highlight the system's potential to advance smart agriculture by increasing yield quality, reducing resource consumption, and enhancing automation. Future work will focus on scaling the system for commercial farms, improving adaptability using machine learning to refine fuzzy rules. It will also integrate renewable energy and AI-based crop health assessment using computer vision. The SMHS framework sets a foundation for intelligent, sustainable mushroom farming and broader smart agricultural applications.