Includes bibliographical references

Calorie tracking applications often rely on manual user input, and this can be tedious and prone to human error. Misinputs can make the whole process feel like a chore. Existing developed system also requires additional actions like shooting in different views or limitations on estimations based on height [1], [2]. To address this, this study aims to develop an automated calorie estimation system that can identify fruits and compute their calorie content without requiring text-based input from the user. The system uses a camera and the YOLOv11 algorithm to identify five common fruits, namely apples, bananas, oranges, strawberries, and kiwis. At the same time, a load cell connected to an Arduino Nano measures the fruit's weight, and calorie data is fetched from FoodData Central. Together, these components create a seamless, hands-free experience for the user. To test how well it actually works, the system was evaluated using a Developmental-Comparative research design. The fruit detection model achieved a 97% mAP, 98% precision, 94.5% recall, and a 96.2% F1 score. Calorie estimates were then compared against MyFitnessPal using paired sample t-tests. Results showed strong agreement for oranges, bananas, and strawberries, though apples and kiwis showed some differences worth noting. Overall, the system proved to be both reliable and fast, making it a genuinely useful tool for health-conscious individuals or for educational purposes. Looking ahead, the findings point toward meaningful improvements like improved sensor accuracy, a wider range of recognized fruits, and eventually, a fully offline version.