# Deep Learning in Food Image Recognition: A Comprehensive Review
## 1 Introduction  
Food plays a vital role in daily life, serving not only as a necessity for survival but also as a cultural and health cornerstone. With the advancement of society, heightened health consciousness has driven an increasing demand for personalized and balanced diets. Consequently, food-related research has emerged as a critical interdisciplinary field, bridging computer vision, nutrition science, and industrial automation. Among these efforts, food image recognition--a core task in fine-grained image analysis--has garnered significant attention for its potential to revolutionize dietary assessment, healthcare management, and intelligent retail systems. Applications range from automated checkout in unmanned restaurants to real-time calorie tracking for patients with chronic diseases and fitness enthusiasts. In computer vision, food images are categorized as fine-grained data due to their inherent complexity [1]. These images typically exhibit small inter-class variations (e.g., subtle differences between regional cuisines) and large intra-class diversity (e.g., varying presentations of the same dish), compounded by the absence of consistent spatial structures. Current research primarily focuses on dish recognition using subsidiary tasks such as ingredient identification, food scene analysis, and cooking method classification are also actively explored. The evolution of food image recognition methodologies can be broadly divided into two eras: manual feature extraction and deep learning-based approaches. Early efforts, such as [PERSON] et al. 's fruit recognition system (1977) [2] and [PERSON] et al. 's Veggie Vision (1996) [3], relied on handcrafted features like color histograms and texture descriptors. Subsequent advancements introduced hybrid frameworks, including [PERSON] et al. 's multimedia food logging system (2008) [4] and [PERSON] et al. 's pixel-level segmentation techniques (2010) [5]. However, these methods struggled with scalability and robustness due to their dependence on explicit feature engineering. A paradigm shift occurred post-2014 with the advent of deep learning. [PERSON] et al. pioneered the application of convolutional neural networks (CNNs) to food recognition (2014) [6], demonstrating superior accuracy over traditional methods. This breakthrough catalyzed rapid innovation. FoodCam (2014) [7] integrated mobile-based calorie estimation, while Im2 Calories (2015) [8] leveraged CNNs for single-image nutrient prediction. Subsequent years witnessed architectural refinements, such as attention mechanisms (2020, 2024) [9; 10], multi-task learning frameworks (2021) [11], and lightweight models (2022, 2024) [12; 13], addressing challenges like fine-grained discrimination and computational efficiency. Notably, recent advancements like GSNet (2024) [14] and self-supervised pre-training (2024) [10] highlight the field's ongoing evolution toward robustness and adaptability (Table S1). This paper systematically reviews the technological trajectory of food image recognition, emphasizing deep learning innovations, dataset development, and real-world applications. Key contributions include a taxonomy of methodologies, an analysis of dataset limitations, and a discussion of emerging trends in edge computing and multi-modal integration. The main contributions of this survey are summarized in the following four aspects:  
* A comprehensive review of food image recognition methods, emphasizing deep learning models. * A systematic organization of existing datasets to aid researchers in selecting appropriate resources. * A discussion of practical applications in nutrition assessment, food services, and safety. * An analysis of current challenges and future directions for research. This survey selects a number of papers (2014-2024) from major databases and adopts a keyword strategy targeting food recognition and deep learning. Priority was given to studies with reproducible results, excluding work lacking empirical validation.