Abstract: 【 Objective 】 This paper aims to review and summarize the research status and development direction of automatic target spraying technology in facility vegetables at home and abroad in the hope of providing theoretical and scientific basis for the development and application of the technology in automatic target spraying robot of vegetable in facility vegetables. 【 Methods 】 Relevant literatures at home and abroad were collected, field research conducted, and after that, they were systematically summarized and sorted out. 【 Results 】 Overseas navigation technology mainly adopts path recognition and intelligent obstacle avoidance technology based on GPS, machine vision, ladar and other technologies, and has been widely applied. In China, it mainly adopts electromagnetic induction, road edge acquisition and road identification technology based on GPS, ladar and vision technology. At the present stage, image recognition, infrared imaging, hyperspectrum and deep learn-based pest identification technologies are mainly used in foreign countries, which are relatively mature. At present, image recognition technology is mainly used in China to identify crops with color, texture and shape features. In the developed countries, machine vision, laser active vision and ultrasonic technology combined with sensor identification are employed to identify target crops, and variable speed spraying technology is applied in crops, such as lettuce, tomato and other crops, domestic scholars have developed greenhouse autonomous spraying robot, using machine vision technology to obtain target pest and disease location information, the nozzle is controlled separately to achieve the effect of accurate target application. 【 Conclusion 】 Navigation technology, pest and disease identification technology and target spraying technology are the core of automatic target spraying technology. Navigation in the greenhouse by using machine vision and laser radar technology compared with the GPS technology is more reliable, flexible, and accurate. In the aspect of pest and disease identification, we should develop pest and disease identification technology combined with hyperspectral and deep learning to improve the efficiency. The identification of target crops and the judgment of dense degree of branches and leaves need more in-depth study.