Deep Learning‐Based Image Recognition for Food Science and Technology: End‐to‐End Workflows and Domain‐Specific Solutions

Deep learning-based image recognition is increasingly reshaping food science, enabling intelligent systems for online grading, rapid quality evaluation, and food safety monitoring. Nevertheless, the adoption of such approaches in food-related domains is often hindered by fragmented practices, limited domain-specific guidance, and methodological inconsistencies. This review synthesizes a modular end-to-end workflow, spanning task formulation, model development, and application, specifically designed for food science. Three representative case studies are examined to illustrate common applications and practices, while highlighting recurring challenges such as limited data availability, inconsistent annotations, and obstacles to submodel integration that constrain reproducibility and scalability. A set of domain-specific strategies is proposed to address these challenges, covering task decomposition, dataset refinement, annotation and label management, image preprocessing, data augmentation, architecture selection, and submodel integration. Looking ahead, we suggest that future efforts prioritize the development of standardized toolkits-task guidance systems, unified modeling toolchains, and model integration platforms-to enable reliable, generalizable, and impactful computer vision solutions for real-world challenges in food science and technology.