Lightweight Interpretable Deep Learning Model for Nutrient Analysis in Mobile Health Applications

Nutrient analysis through mobile health applications can improve dietary choices, particularly among vulnerable populations. Current mobile nutrient analysis applications face critical limitations: sophisticated deep learning models require substantial computational resources unsuitable for budget devices, while lightweight solutions sacrifice accuracy and lack interpretability necessary for user trust. We develop a lightweight interpretable deep learning architecture combining depthwise separable convolutions, Shuffle Attention mechanisms, and knowledge distillation with integrated Grad-CAM and LIME explanations for real-time interpretability. Our model achieves 97.1% food recognition accuracy (98.0% with cross-validation) and 7.2% mean absolute error in nutrient estimation while maintaining an 11 MB footprint and 150 ms inference time. Knowledge distillation reduces the model size by 62% and energy consumption by 36% while improving accuracy by 2.2 percentage points over non-distilled training. Targeted optimisation for food security categories achieves 94.1% accuracy for staple foods, 93.2% for affordable proteins, and 92.8% for accessible produce. Interpretability methods demonstrate 0.91 feature consistency scores with 38–45 ms explanation generation. These results demonstrate the first mobile nutrient analysis system combining state-of-the-art accuracy with computational efficiency suitable for resource-constrained deployment, addressing accessibility barriers for vulnerable populations.