  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 are not 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 a list of ingredients is very difficult for a beginner cook.
However, it can be a problem even for experts. One such example is recognising
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 ingredients
recognition and designed an algorithm for recommending recipes based on
recognised ingredients. We made a custom dataset consisting of 9856 images
belonging to 32 different food ingredients classes. Convolution Neural Network
(CNN) model was used to identify food ingredients, and for recipe
recommendations, we have used machine learning. We achieved an accuracy of 94
percent, which is quite impressive.
