Bananas, being highly perishable fruits, demand precise control over ripening conditions to maintain quality and extend shelf life. The ripening process of bananas is highly temperature- and humidity-dependent. Conventional cold storage methods often result in inconsistent ripening, reduced shelf life, and significant economic losses. This study presents an IoT-based intelligent cold storage control system for optimizing banana ripening. Operational flexibility and efficiency are ensured by the IoT's ability to monitor and control remotely. Within a ripening chamber, the system uses wireless sensors to track temperature, humidity, ethylene levels, and CO2 data. Real-time data is then sent to a cloud-based platform. In order to guarantee uniform ripening, machine learning algorithms automatically change temperature and humidity levels based on data analysis to predict ideal ripening circumstances. The findings of the experiment show a notable increase in shelf life, decreased energy use, and improved ripening consistency. This cutting-edge system improves food quality, lowers waste, and supports sustainable farming methods, providing a promising solution for the banana supply chain.
machine learning, smart agriculture, cold storage, banana ripening, and the Internet of Things
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