The assessment of food intake is an important aspect in the promotion of healthy living, particularly in Nigeria, wherethe challenges that exist in the estimation of the energy value of food consumed have led to the increase of lifestylediseases such as obesity, diabetes, and heart-related problems. This research aimed at addressing the problem of foodestimation through the creation of a machine learning model for the estimation of the calories contained in raw foodconsumed in Nigeria. The model was developed based on the use of a wide range of food items, 184, which exist inNigeria. These food items were used, rotated, flipped, and zoomed to improve the accuracy of the model. The CNNalgorithm was used for the classification. The accuracy of the model was tested using the Mean Absolute Error, MeanSquare Error, and R-square value. The model achieved an R-square value of 0.99. The accuracy of the model wasvalidated based on the existing studies that have been conducted on the estimation of calories through the use ofimages of food. The model developed can be used for the control of diet for patients on regulated nutrition.
Osunade et al. (Wed,) studied this question.