Investigation of calorimetry burned in food using image processing and IoT

Calorimetry is an avenue for exploring the energy change due to heat involved between the various reaction stages at chemical, physical, or phase transition levels. Calorimetry seeks to determine the calories contained in food items within food science. However, classical calorimetric analyses are time-consuming and require a good amount of human intervention. The concept of image processing, involving the YOLO object detection algorithm, along with IoT technologies are a completely new domain in automating calorimetric measurements in food with high accuracy. YOLO's fast and efficient detection of the object allows accurate identification and tracking of food samples during the analysis. The project aims to develop a system that will efficiently and accurately measure the caloric value of carbonized food samples using image processing using YOLO and IoT-unified data collection method to automate the whole data gathering process and achieve precision in measurement and with monitoring and analysis in real time.