Processed food has become an indispensable part of the human food chain. It provides rich nutrition for human health and satisfies various other requirements for food consumption. However, establishing traceability systems for processed food faces a different set of challenges compared to primary agro-food, because of the variety of raw materials, batch mixing, and resource transformation. In this paper, progress in the traceability of processed food is reviewed. Based on an analysis of the food supply chain and processing stage, the problem of traceability in food processing results from the transformations that the resources go through. Methods to implement traceability in food processing, including physical separation in different lots, defining and associating batches, isotope analysis and DNA tracking, statistical data models, internal traceability system development, artificial intelligence (AI), and blockchain-based approaches are summarized. Traceability is evaluated based on recall effects, TRUs (traceable resource units), and comprehensive granularity. Different methods have different advantages and disadvantages. The combined application of different methods should consider the specific application scenarios in food processing to improve granularity. On the other hand, novel technologies, including batch mixing optimization with AI, quality forecasting with big data, and credible traceability with blockchain, are presented in the context of improving traceability performance in food processing.

Global food trade has become an increasingly crucial element for feeding the world's population. Enhancing bilateral or multilateral trust in food safety in international food trade is not only important for promoting the sustainable development of trade but is also beneficial for cooperation when facing a global food crisis. However, highly credible traceability systems (TSs) for the cross‐border movement of food are still absent in many countries and regions. Blockchain is regarded as a promising technology that can help build trust for transparency and security issues. In this paper, an interconnected conceptual traceability framework based on blockchain is proposed in order to increase trust in food safety during food trade. Taking the food trade between China and the European Union as an example, a conceptual framework is designed in order to take full advantage of existing TSs in these two locations, and the features of logistical flow, data flow, and blockchain flow are analyzed. Considering the data capacity and data privacy level, a hybrid data storage method combining on‐chain and off‐chain is adopted. Smart contracts according to the features of cross‐border food trade—including the recording of exportation data, exporter inspection data, shipment data, importer inspection data, importation data, and tracing queries—are packaged and deployed to a blockchain network. An effective operation mechanism involving the distribution of related rights for different roles is presented. The blockchain‐based TS framework has the advantages of enhancing bilateral trust in cross‐border food trade, providing a flexible and intelligent technical framework, and having effective operability. Future challenges, such as data security, special smart contracts, and consensus mechanisms, and interoperability with other systems, are discussed.

As the global fruit growing area continues to increase and the population aging problem continues to intensify, fruit and vegetable production is constrained by the difficulties of labor shortages and high costs. Single-arm harvesting robots are inefficient, and in order to balance harvesting accuracy and efficiency, research on multi-arm harvesting robots has become a hot topic. This paper summarizes the performance of multi-arm harvesting robots in indoor and outdoor environments from the aspects of automatic navigation technology, fruit and vegetable identification and localization, multi-arm workspace optimization, and multi-arm harvesting task planning and analyzes their advantages and challenges in practical applications. The results show that the lack of application of automatic field navigation for multi-arm harvesting robots, the low harvesting rate in non-structured environments, and the complexity of algorithms for multi-arm harvesting robots’ task planning are the main challenges hindering their wide-scale application. Future studies need to focus on building a standardized growing environment to control the amount of information acquired by the robots and optimize the multi-arm control strategy of these challenges, which is an important direction for research on multi-arm harvesting robots.