• Krishnali Kondekar
• Sushma Ghose

In order to monitor the calorie intake and prevent the onset of various illnesses, such as weight gain, obesity, diabetes, high cholesterol, and heart attacks, which can result from an imbalanced diet, careful monitoring of food calorie estimation is important. To accurately measure calories in food, it is essential to utilize image processing systems for the detection of food ingredients. It is often challenging for individuals to accurately determine the calorie content of their meals. While they can seek assistance from experts to estimate the calorie count, this approach is not always practical and does not provide real-time information prior to consuming the meal. Thus this paper presents a Food Calorie Estimation System from Images using Convolutional Neural Networks (CNNs). The system captures an image of the food like Breakfast, Lunch, Evening Meal and Drinks and Snacks, and through the implementation of FCTH and the CEDD feature, the Convolutional Neural Network identifies the food type such as idli , Dosa, Medu vada, Samosa and poha. The system conveniently displays the calorie count of the captured food. The system shows the accuracy of 91.82% in detecting the calorie count of captured food image.

Food Classification, Calorie Estimation, Calorie count, Image Processing, FCTH Features, CEDD feature, Convolutional Neural Networks (CNNs).

"Implementation of Real-Time Food Calorie Estimation system using Image processing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e563-e571, April-2024, Available :http://www.jetir.org/papers/JETIR2404461.pdf

"Implementation of Real-Time Food Calorie Estimation system using Image processing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe563-e571, April-2024, Available at : http://www.jetir.org/papers/JETIR2404461.pdf