Fruit and Vegetable Detection with Calorie Estimation Built on Mobilenetv2

• Published in: 2024 Second International Conference on Advances in Information Technology (ICAIT) Vol. 1; pp. 1 - 6
• Main Authors: N, Sengottaiayn, S, Hamsanandhini, R, Rajesh Sharma, Sungheetha, Akey, M, Monika, Adare, Ketema
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24-07-2024
• Subjects: Accuracy; Architecture; Buildings; CNN; Computer architecture; Computer vision; Deep learning; EfficientNet; Estimation; Fruit and vegetable Recognition; Information technology; MobileNetV2; Monitoring; Neural networks
• DOI: 10.1109/ICAIT61638.2024.10690758

Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building upon the success of neural network models in various domains, this study explores the application of Mobilenetv2 and EfficientNet architecture for accurately identifying fruits and vegetables from images and estimating their respective caloric content. The research dataset comprises meticulously curated images of diverse fruits and vegetables, ensuring comprehensive coverage across different categories. Through rigorous experimentation and evaluation, the proposed model demonstrates remarkable accuracy in fruit and vegetable recognition, achieving an impressive accuracy rate of 97.6%. Moreover, the incorporation of calorie estimation adds a novel dimension to dietary analysis, enabling users to make informed decisions regarding their nutritional intake. The findings of this research not only contribute to the advancement of computer vision techniques but also hold significant implications for personalized nutrition tracking and health- conscious applications.