Authors: Mr. Boddu Pavan Kumar, Miss. Savarapu Suhasini
Abstract: The increasing demand for intelligent dietary assessment and nutrition monitoring has accelerated research in automated food image classification systems. However, accurately identifying food categories remains challenging due to significant variations in appearance, illumination, background complexity, and similarities among visually related food items. Conventional machine learning techniques, which rely on handcrafted feature extraction, often fail to capture the intricate visual characteristics required for robust food recognition. To overcome these limitations, this paper presents a hybrid food image classification framework that integrates transfer learning-based feature extraction with machine learning classifiers. Pre-trained deep learning architectures, including EfficientNet, DenseNet, and MobileNet, are employed to learn rich and discriminative visual representations from food images without requiring extensive model training. The extracted deep features are subsequently processed using advanced machine learning algorithms such as Random Forest and XGBoost to perform accurate food category prediction. This hybrid strategy effectively combines the representational strength of deep neural networks with the computational efficiency and interpretability of classical machine learning methods. Experimental evaluation demonstrates that the proposed framework achieves superior classification accuracy, precision, recall, and F1-score compared with conventional image classification approaches. Furthermore, the model exhibits improved robustness when handling diverse food images captured under varying environmental conditions. The proposed framework has significant potential for practical deployment in applications such as intelligent nutrition monitoring, automated calorie estimation, healthcare support systems, and smart dietary recommendation platforms, contributing to the development of reliable AI-driven food analysis solutions.
