## 1 Introduction  
In recent years, the booming fruit-growing industry has made fruit picking an important production segment that employs more than 60% of the labor force. With the aging population and urbanization further highlighting the labor shortage, a significant increase is seen in the cost of picking [1]. Therefore, intelligent agricultural fruit-picking equipment and picking robots that can improve picking efficiency and reduce picking costs have now become an important research direction. Figure 1 sets out several representative fruit-picking robots.  
For picking robots, the recognition ability of the vision system is particularly important in the picking process. Identifying the fruit efficiently and accurately is a prerequisite for completing the picking task [2]. In 1968, [PERSON] and [PERSON] [3] introduced machine vision to fruit recognition, which enabled the rapid development of fruit-harvesting robots, including the first vision recognition system for apples established by [PERSON] et al. [4] in 1977. Traditional digital image processing techniques and target recognition techniques based on deep learning make up the majority of the approaches used to identify fruits. These techniques extract features such as color, geometric shape, and texture in images for matching. Such methods have matured after researchers invested years in them. However, the actual working environment of picking robots is much more complex. For example, the change in illumination conditions easily affects the color features of the fruits in images, and the occlusion of the fruits by background objects such as branches and leaves easily affects the geometric features of the fruits in images. This brings problems such as low recognition accuracy, poor model real-time, and low robustness under complex environments. Therefore, automated picking experiments on picking robots pose challenges to conventional digital image processing techniques. In 2012, the AlexNet network was proposed and won the ImageNet image recognition competition with excellent performance. Since then, deep learning has received a lot of attention from academics and has been extensively used to recognize fruit targets. [PERSON] et al. [5] achieved the recognition of dates by combining AlexNet and VGG16 networks, and the recognition accuracy exceeded 90% in the unobstructed cases. The deep-based target recognition method uses a multilayer perceptron structure, where both low-level features and high-level features are analyzed [6] and used to recognize targets. Therefore, compared to traditional digital image processing techniques, this method offers higher recognition accuracy and greater robustness, versatility, and generalization [7].  
Although deep learning technology has been widely used in fruit recognition, it still cannot meet the needs of picking robots in the actual working environment, as they cannot fully address the collision between the end-effector and the obstacle objects, such as branches, during the fruit-picking process. Therefore, it is necessary to reconstruct the fruits and branches in 3D to obtain 3D information. In this paper, we summarize the target recognition techniques of picking robots over the past few years by focusing on analyzing and combing through the advantages and disadvantages of different fruit recognition methods. Figure 2 shows the species and quantity of fruit involved in the citation. In addition, this paper summarizes the problems and challenges of target recognition technology for picking machines and proposes the future development trend of this technology for feasibility reference by other researchers.  
Figure 1: Representative forms of picking robots. (**a**) Litchi-harvesting robot (Adapted with permission from Ref. [8]. 2020, [PERSON] et al.); (**b**) Apple-harvesting robot (Adapted with permission from Ref. [9]. 2015, [PERSON] et al.); (**c**) Another litchi-harvesting robot (Adapted with permission from Ref. [10]. 2020, [PERSON] et al.).