AI-Powered Recipe Generator from Food Images Using Deep Learning

The automatic generation of cooking recipes from food images has gained significant attention in the field of food computing and artificial intelligence. This research presents a deep learning-based approach for recipe generation from food images, achieving an accuracy of 92.7%. The proposed model utilizes computer vision techniques to analyze food images and predict essential recipe components, including the recipe title, ingredients, and step-by-step cooking instructions. A combination of Convolutional Neu- ral Networks (CNNs) and Transformer-based architectures enhances the system’s ability to understand complex food compositions. The dataset used comprises diverse food categories, ensuring robust gen- eralization across various cuisines. Performance evaluation against benchmark datasets highlights the model’s superiority in generating coherent and contextually accurate recipes. Comparisons with state-of- the-art models, including Inverse Cooking and FIRE, demonstrate improvements in ingredient prediction and instruction coherence. Despite achieving high accuracy, challenges such as ingredient ambiguity and complex dish representations persist. Future work aims to refine multimodal learning approaches and integrate real-time food recognition for enhanced user experience. This study contributes to advanc- ing AI-driven food recommendation systems, bridging the gap between computer vision and culinary knowledge.