AI-Based Temperature Automation and Location Tracking in Ship Container Monitoring System (TALT)

In a world increasingly reliant on technology, the need for innovative solutions in various aspects of daily life is paramount. This project presents an advanced system that integrates artificial intelligence (AI) and temperature control technology to address the critical domain of food storage. To achieve food identification, a deep learning model, specifically a Convolutional Neural Network (CNN), is trained on a diverse dataset of food images. When a new item is introduced into the container, a Pi camera captures an image, and the AI model instantly identifies the food type. Simultaneously, the system employs a Proportional Integral Derivative (PID) controller to regulate the temperature using a Peltier device. Temperature set points for each food category are predefined or learned over time, ensuring precise temperature control. Incorporating GPS location tracking further enhances the system’s utility. By integrating a GPS module, the container’s location can be accurately determined and monitored in real time. In previous work, it has a monitoring system using IOT-based technology.