Date of Publication :8th August 2024

Abstract: In the realm of dietary analysis and nutritional monitoring, the utilization of Convolutional Neural Networks (CNNs) has emerged as a pivotal advancement. This paper presents a thorough investigation into the advancements and breakthroughs achieved through the application of CNNs in the realms of food recognition and calorie estimation. The burgeoning significance of automated dietary analysis necessitates a robust framework capable of accurately identifying food items from images and estimating their calorie content, thereby facilitating informed dietary choices and promoting overall well-being. CNNs exhibit remarkable proficiency in discerning intricate patterns within food images, enabling real-time examination and monitoring of dietary consumption. Furthermore, the integration of calorie estimation capabilities within CNN architectures empowers individuals with vital nutritional information, fostering a deeper understanding of their dietary patterns and facilitating adherence to balanced nutrition guidelines. Despite inherent challenges, including variations in food presentation and image quality, the utilization of CNN algorithms showcases immense potential in revolutionizing dietary analysis and empowering individuals to make informed decisions regarding their health and wellness. This research not only contributes to the advancement of computer vision techniques but also aligns with Sustainable Development Goal 3 – Good Health and Well-being, by promoting informed dietary choices and supporting the attainment of balanced nutrition and calorie management objectives.

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