Enhancing cold storage infrastructure efficiency through IoT, image processing and solar panel.

The quality of food is significantly influenced by temperature. In Cold Storage facilities, it is essential to maintain optimal chamber freezer conditions within a specific temperature range. To achieve this, regular automatic measurement of the freezer temperature becomes necessary, although it can be time-consuming, it ultimately enhances crew efficiency. Hence, the implementation of an automatic system for measuring freezer temperature is significant in alleviating the workload of the crew when it comes to monitoring freezer temperature. The boosting demand for viable and efficient cold storage has led to the advancement of new techniques and technologies. Vintage refrigeration systems are known for their high-energy consumption and substantial aid to the release of greenhouse gases. This proposed paper introduced a novel optimization tactic for cold storage, incorporating Internet of Things (IoT) technology and solar panels to minimize both energy expenses and the environmental influence of greenhouse gas emissions. The proposed system comprises essential components, including a DHT11 temperature and humidity sensor, a gas sensor, and a microcontroller, responsible for data collection and refrigeration system regulation along with a battery connected to a solar panel. The power supply section of proposed system is derived from a solar panel array, utilizing daytime sunlight and storing surplus energy in a battery for nighttime operation. Real-time data analysis enables prompt adjustments to the refrigeration system, ensuring optimal storage conditions for the products. A remarkable advantage of the proposed system is its self-governance from the electrical grid, providing independence in operation. This holds significant advantages in regions with unstable electricity provision or areas burdened by expensive power tariffs. These results underscore the considerable potential of combining IoT technology and solar panels to optimize cold storage systems, contributing to a more sustainable future. Moreover, a machine learning based approach has been developed which can classify ripe and unripe fruits/vegetables. Statistical feature extraction is followed by classification using a deep neural network. It has been shown that the proposed approach achieves an accuracy of 97% thereby rendering high classification accuracy. In summary, our results exemplify the immense potential of combining IoT and solar panels to optimize cold storage systems, fostering a more sustainable future. Furthermore, the automated classification mechanism would facilitate the continuous monitoring of fruits/vegetables thereby reducing the chances of spoilage. [ABSTRACT FROM AUTHOR]