Food Recognition and Calorie Assessment from Images Using Convolutional Neural Networks


  • Yogeshvari Makwana, Sailesh Iyer, Sanju Tiwari


Food recognition, Deep convolutional neural network, Artificial neural network, Calorie Assessment, Food Segmentation, Food classification, Image processing, Computer vision,Calorie Estimation


In human life there are many things that are required to live but there are three basic needs of every human. These three needs are a must in every human life that are food, clothes and house. Nowadays humans are very much interested in eating different types of foods. Interested to know different kinds of foods from different places. We have developed a food recognition and calorie estimation system that utilises images of food provided by the user to recognize the food item and estimate its calorie content. Food image recognition is a promising application of visual object recognition in computer vision. Our system leverages image processing techniques and computational intelligence to accurately recognize food items. We have trained a large and deep convolutional neural network using a dataset of 1000 high-resolution images for each food category. The trained CNN is capable of classifying the input food images with high accuracy, enabling accurate recognition of the food items. Additionally, our system incorporates calorie estimation algorithms to estimate the calorie content of the recognized food items, providing valuable information for users concerned about their nutritional intake. Overall, our food recognition and calorie estimation system offers an efficient and effective solution for automatically recognizing food items and estimating their calorie content using state-of-the-art deep learning techniques.


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How to Cite

Yogeshvari Makwana. (2024). Food Recognition and Calorie Assessment from Images Using Convolutional Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 3432–3442. Retrieved from



Research Article