CNN-Driven Nutritional Analysis: Predicting Food Composition Via Image Processing Techniques

As for the contribution of this research, an Automatic Nutrient Prediction System (APS) is introduced, which applies deep learning with CNN to recognize food images for nutritional advice. Using the large Food-101 dataset of 101,000 images split into 101 food classes, the proposed CNN model comprises five Convolutional Layers Conv2D, MaxPooling; and Subsampling Layers (values of 0.25 and 0.4) to combat overfitting. Training data is normalized across RGB channels with the sizes of the images decreasing to 128 x 128 pixels. The data is augmented through rotation, translation, and brightness changes. Training accuracies of 98.76 and 98.87 across epochs demonstrate the model's potential for future improvements including Vision Transformers with RGB-D, for sharp volumetric accuracy and inference. The applications include foods that will be recommended for an individual's proper diet based on this system in the health and wellness industry.