RecipeLens: Revolutionizing Meal Preparation with Image-Based Ingredient Detection and Recipe Suggestions

In today's fast-paced lifestyle, the importance of preparing meals at home has significantly increased due to the high costs and time constraints associated with dining out. However, while home-cooked meals offer numerous benefits, such as cost-effectiveness and convenience, maintaining variety in one's diet is crucial to preventing culinary monotony. Contrary to common belief, creating diverse recipes does not necessarily require an extensive array of ingredients; rather, innovative combinations and techniques can transform familiar ingredients into exciting culinary experiences. This research paper introduces RecipeLens, a novel approach that combines food ingredient detection with recipe suggestions to streamline the cooking process and enhance culinary creativity. Building upon existing studies in food ingredient detection and recipe recommendation, RecipeLens integrates both functionalities into a unified platform, offering users a seamless and efficient way to discover and prepare diverse dishes using available ingredients. To expedite ingredient classification, RecipeLens employs the MobileNet_v2 model, renowned for its accuracy and efficiency in image recognition tasks. Additionally, recipe suggestions are sourced from the Food Recipe API, ensuring the validity and relevance of each recommended dish. Furthermore, RecipeLens accommodates users with limited knowledge of fruit and vegetable names, making it accessible to individuals of varying culinary expertise. Through the integration of ingredient detection and recipe recommendation, RecipeLens empowers users to unleash their culinary creativity, discover new dishes, and enjoy diverse homemade meals with ease and efficiency.