Optimizing Food Nutrient Testing Data Analysis and Model Building Using Machine Learning Algorithms

Food nutrient testing technology is a key link in health management and disease prevention, and traditional methods rely on chemical analysis and manual experience, which have problems such as low efficiency, high cost, and error accumulation. In recent years, the rapid development of machine learning and computer vision technologies has provided new ideas for automated and high-precision food nutrition assessment. Existing research focuses on unimodal image analysis, but in practical scenarios with varied food presentations, complex lighting, and frequent occlusions, it is difficult to accurately characterize nutrient distribution with a single visual feature. Multimodal data fusion has become an important direction to break through the bottleneck. The RGB-D information acquired by depth sensors can synchronously capture the surface texture and three-dimensional structure of food products, which provides the basis for modeling multidimensional feature associations. However, how to effectively integrate different modal features and solve the cross-scale data alignment problem still requires in-depth research. In this paper, we propose a fusion network based on RGB-D Net to explore the optimization path of food nutrient detection through multi-stage feature extraction and adaptive fusion mechanism.