02547nam a22002417a 4500 OSt 20261110111054.0 261110s2026 |||||||| |||| 00| 0 eng d T ENG A55 2026 Aninon, Ken Levy F. Raspberry pi-based fruit calorie counting system using load sensor and computer vision : a developmental-comparative design project / Ken Levy F. Aninon, Julius Robert R. Cortes, Juan Miguel B. Manalo, and Raha C. Pullon. EAC Cavite : School of Engineering, 2026. xiii, 130 pages ; 28 cm. rdacontent text rdamedia unmediated rdacarrier volume 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. author Cortes, Julius Robert R. author Manalo, Juan Miguel B. author Pullon, Raha C. lcc TH EAC Cavite-Thesis 0 100691 100691 0 0 lcc 0 0 CAVITE CAVITE EAC Cavite-Thesis 2026-11-10 0 T ENG A55 2026 ML-TH2834 2026-11-10 11:10:30 2026-11-10 TH