Banana ripening is a critical process that requires precise temperature, humidity, and ethylene control to ensure optimal fruit quality. This paper presents an IoT-based real-time monitoring and control system for banana ripening chambers. The system integrates wireless sensors to collect temperature, humidity, ethylene, and CO2 data, which is transmitted to a cloud-based platform for real-time monitoring and analysis. Machine learning algorithms are employed to predict optimal ripening conditions and automate control of temperature, humidity, and ethylene levels. Experimental results demonstrate the effectiveness of the proposed system in reducing ripening time, improving fruit quality, and minimizing energy consumption. The system's scalability, flexibility, and cost-effectiveness make it an attractive solution for banana ripening facilities.
Banana ripening, IoT, real-time monitoring, machine learning, temperature control, humidity control, ethylene control, CO2 monitoring.
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