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.

Machine vision and near-infrared light technology are widely used in fruits and vegetable grading, as an important means of agricultural non-destructive testing. The characteristics of fruits and vegetables can easily be automatically distinguished by these two technologies, such as appearance, shape, color and texture. Nondestructive testing is reasonably used for image processing and pattern recognition, and can meet the identification and grading of single features and fusion features in production. Through the summary and analysis of the fruits and vegetable grading technology in the past five years, the results show that the accuracy of machine vision for fruits and vegetable size grading is 70–99.8%, the accuracy of external defect grading is 88–95%, and the accuracy of NIR and hyperspectral internal detection grading is 80.56–100%. Comprehensive research on multi-feature fusion technology in the future can provide comprehensive guidance for the construction of automatic integrated grading of fruits and vegetables, which is the main research direction of fruits and vegetable grading in the future.