Abstract
The food industry prioritizes quality control and product knowledge, emphasizing factors like quantity, freshness, and color. This research addresses Sustainable Development Goals (SDGs) focused on controlling food consumption, promoting health, reducing energy usage, and minimizing environmental impact. The primary objective was to utilize machine vision and deep learning to identify consumed food products. The study categorizes food into 32 classes, divided into three main categories, and includes the documentation of images and videos captured during consumption across various situations. Initially, the dataset comprised 12,000 images in 16 classes and 24,000 images in 32 classes, which were subsequently augmented to yield 60,000 and 120,000 images, respectively. The augmented datasets were then processed through nine popular deep learning architectures, identifying ResNet50, EfficientNetB5, B6, and B7 as the most effective architectures. An essential step involved updating hyperparameters, including image size, batch size, learning rate, and optimizer settings, to enhance convergence rates and accuracy. The EfficientNetB7 model was adapted for further testing and compared against two prominent optimizers, Adam and Lion. Ultimately, the EfficientNetB7 model with the Lion optimizer was chosen for the dataset. The results of this deep learning algorithm demonstrated remarkable performance, achieving 100% accuracy in identifying images of food-consumed products within 16 classes when using EfficientNetB7 and the Lion optimizer. For the 32-class case, the accuracy reached 99%, with the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) recorded at 0.0079, 0.035, and 0.18, respectively. These findings illustrate the robustness of the adjusted dataset in alignment with the designed deep learning architecture.