# Deep Learning in Food Image Recognition: A Comprehensive Review
## 5 Open Issues and Future Direction
Examples include intelligent retail and unmanned settlement systems, automated food production processing, and agricultural picking and sorting robots. [PERSON] et al. developed a cooking assistance system to identify ingredient types and provide suggested recipes to the user [33]. In addition, food image recognition technologies, such as kitchen cooking robots and intelligent refrigerator food ingredient recognition and management, have gradually become more and more widely used in families. With the popularity of portable smart devices, the market demand for lightweight food recognition technology for mobile terminals is increasing [34]. At present, there are also some open source tools. For example, [PERSON] et al. [35] proposed an open source dataset for fine-grained food classification, containing 251 kinds of fine-grained food and 158k images, which has been used in international competitions. [PERSON] et al. [36] proposed FoodAI, an open source smart food recording system that provides pre-trained models and API interfaces for mobile applications. At the level of technical implementation, the lightweight and real-time performance of deep learning models have become the key. Although previous studies have proposed mobile solutions based on MobileNetV3 [37] and EfficientNet [38], model compression and edge deployment in cross-cultural scenarios still need to be further optimized. Food is a special carrier of culture and health. On the one hand, through the analysis of food imagery, the transmission path of traditional food culture is traced. On the other hand, it can provide objective dietary evaluation means for chronic disease management [39], which further highlights the application value of this technology. However, the deep application of technology also brings new ethical challenges. Diet data are sensitive health information, and privacy protection problems are becoming increasingly prominent. A user's diet may reveal health conditions, and there is a risk of data misuse. Dietary preference analysis in cross-cultural scenarios should follow local data protection regulations. In some countries, some generative AI service management policies have been introduced to regulate applications. In commercial applications, personalized recommendation algorithms may exacerbate dietary bias. With the increasing maturity of recognition technology, it is urgent to establish a development framework that takes into account both technological innovation and ethical constraints while expanding emerging markets, so as to realize the balance between technical benefits and social responsibility. Current research faces challenges such as dataset limitations, insufficient model generalization, and ethical privacy issues. In the future, we need to focus on the following: developing large-scale datasets with multicultural, fine-grained annotations. Multi-modal fusion is used to improve the recognition performance by combining multi-source data such as vision, text, and geographic information. The model was optimized to adapt to mobile terminal and edge computing scenarios and improve the lightweight and real-time performance of the model in practical applications. In addition, it is also an important research direction to establish a data protection framework, balance technological innovation and social responsibility, and ensure that the technology will not infringe on the privacy of others in practical applications.