Hybrid Dryer and Machine Learning Enhance Efficiency of Ganoderma Lucidum Mushroom Drying

A new research study has demonstrated the effectiveness of a hybrid-powered dryer in reducing the weight, moisture content, and microbial load of Ganoderma lucidum mushrooms. The system, which incorporates an Internet of Things (IoT) platform, enables real-time monitoring of temperature, time, and humidity. Researchers from Rajamangala University of Technology Lanna have utilized machine learning models, including random forest regression, decision tree regression, and multiple linear regression, to optimize the drying process.

Key Takeaways:

• The hybrid dryer achieved significant reductions in weight, moisture content, and microbial load of Ganoderma lucidum mushrooms, with higher temperatures being most effective in reducing microbial counts.
• The study evaluated the performance of three machine learning models: random forest regression, decision tree regression, and multiple linear regression, with random forest regression exhibiting the highest accuracy in estimating bacterial levels.
• The study concluded that the selected machine learning model, multiple linear regression, was most suitable for IoT applications due to its simplicity in real-time implementation on devices.
• The hybrid dryer system has the potential to be scaled up for industrial processing facilities.
• The study demonstrated the efficiency of the hybrid dryer and highlighted the potential of machine learning models to optimize the drying process, contributing to energy efficiency and product quality control.

Statistics:

• The study achieved near-zero microbial counts after 240 to 480 minutes at a temperature of 80°C.
• The machine learning model, random forest regression, exhibited an accuracy of 90% in estimating bacterial levels.
• The study evaluated the performance of the machine learning models across temperatures ranging from 40°C to 80°C.
• The study concluded that the hybrid dryer system achieved a 30% reduction in drying time compared to traditional drying methods.
• The study highlighted the potential for scaling up the hybrid dryer system for use in industrial processing facilities.

Sources:

• Estimation of microbial load in Ganoderma lucidum using a solar-electric hybrid dryer enhanced by machine learning and IoT. Smart Agricultural Technology, 2025,11():100977.
• Life Science Weekly, August 12, 2025; p 7633.