Development of an Integrated Mushroom Cultivation System Based on Sensor Array and Machine Learning

The cultivation of oyster mushrooms (Pleurotus ostreatus) in Indonesia has expanded rapidly and has become one of the country’s prominent horticultural commodities. According to the Directorate General of Horticulture, the demand for oyster mushrooms continues to increase for both domestic consumption and export. Oyster mushroom farming is highly promising for further development due to its economic value, environmental friendliness, and suitability for small- to medium-scale agribusiness. However, farmers often struggle to meet the growing demand because of inadequate infrastructure and limited environmental control technologies. A major challenge in oyster mushroom cultivation is the strict and highly sensitive microclimate requirements—particularly temperature, humidity, air circulation, and light exposure. Even minor deviations in these parameters can significantly affect yield, quality, and biological efficiency. To address these challenges, precision agriculture (PA) offers an effective solution through the integration of intelligent sensing technologies and automated environmental control systems. This study aims to develop an Integrated Mushroom Cultivation System that leverages sensor arrays and Machine Learning to optimize microclimate regulation. The system records environmental data—including light intensity, temperature, and humidity—in a big-data structure, enabling multi-sensor evaluation to generate more accurate environmental decisions. Field data collected from 1–25 August 2025 indicate that the microclimate within the cultivation chamber was relatively stable, with an average temperature around 24 °C. Humidity conditions remained within the Optimal Fruiting range at 80.44%. Meanwhile, peak light-intensity readings reached 55,000–60,000 lumens due to direct sensor exposure to the light source, rather than representing the actual illuminance at the substrate surface. To ensure reliable automated decision-making—particularly for misting and environmental adjustments—sensor calibration and anomaly detection mechanisms must be implemented, focusing especially on parameters that directly influence mushroom growth. The adoption of these recommendations is expected to enhance environmental stability within the cultivation chamber and improve production quality, supporting the development of adaptive systems for precision agriculture.