# Food Recipe Recommendation Based on Ingredients Detection Using Deep Learning

arXiv: https://arxiv.org/abs/2203.06721 · HTML: https://mlsanity.com/paper/2203.06721/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 13 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [On Loss Functions for Deep Neural Networks in Classification](https://mlsanity.com/paper/1702.05659/) (2017)
- [Inverse Cooking: Recipe Generation from Food Images](https://mlsanity.com/paper/1812.06164/) (2018)
- [DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment](https://mlsanity.com/paper/1606.05675/) (2016)
- [Image to Bengali Caption Generation Using Deep CNN and Bidirectional Gated Recurrent Unit](https://mlsanity.com/paper/2012.12139/) (2020)
- [Yelp Food Identification via Image Feature Extraction and Classification](https://mlsanity.com/paper/1902.05413/) (2019)

## Built on by (most cited first, 2 of 2)

- [FIRE: Food Image to REcipe generation](https://mlsanity.com/paper/2308.14391/) (2023)
- [Improving Fine-Grained Rice Leaf Disease Detection via Angular-Compactness Dual Loss Learning](https://mlsanity.com/paper/2603.25006/) (2026)

## Abstract (arXiv)

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.