Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning

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Md. Kishor Morol, Md. Shafaat Jamil Rokon, Ishra Binte Hasan, A. M. Saif, Rafid Hussain Khan, Shuvra Smaran Das

Published: 2022, Last Modified: 12 Dec 2025ICCA 2022EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Food is essential for human survival, and people always try to taste different types of delicious recipes. Frequently, people choose food ingredients without even knowing their names or pick up some food ingredients that aren’t obvious to them from a grocery store. Knowing which ingredients can be mixed to make a delicious food recipe is essential. Selecting the right recipe by choosing from a list of ingredients is very difficult for a beginner cook. However, it can be a problem even for experts. There is the constant use of machine learning in our everyday lives. One such example is recognizing objects through image processing. Although this process is complex due to different food ingredients, traditional approaches will lead to an inaccuracy rate. These problems can be solved by machine learning and deep learning approaches. In this paper, we implemented a model for food ingredient recognition and designed an algorithm for recommending recipes based on recognized ingredients. We made a custom dataset consisting of 9856 images belonging to 32 different food ingredient classes. A Convolution Neural Network (CNN) model was used to identify food ingredients, and for recipe recommendations, we used machine learning. We achieved an accuracy of 94%, which is quite impressive.