Research and Application of Mushroom Growth Status Monitoring Technology

Maintaining optimal growth conditions is a critical prerequisite for enhancing yield and profitability in modern mushroom cultivation facilities. However, existing cultivation environments are characterized by complexity, with challenges in the stability and accuracy of environmental data monitoring. Manual assessment of mushroom growth status is further hindered by inefficiency, high costs, and low precision. To address these challenges, this study conducts research on mushroom growth status monitoring technologies. A Mushroom Cultivation Room and Growth Status Monitoring Terminal was designed and developed to resolve data acquisition and processing issues for environmental parameters (e.g., temperature, humidity, $\text{CO}_{2}$ levels) and substrate biomass metrics. A lightweight GhostConv module was introduced to optimize the YOLO11s model, enabling dynamic monitoring and analysis of growth indicators such as cap dimensions and maturity levels. The optimized model was deployed on a Mushroom Identification App, facilitating longterm monitoring and predictive analytics for mushroom classification and growth status at the Ningxia Dawukou Mushroom Technology Hub (NDMTH). This system provides a scientific decision-making framework for mushroom cultivation.