Abstract: Machine vision technology is the key of fruit target recognition and positioning. Traditional target recognition algorithm has low accuracy and slow detection speed, which is difficult to meet the needs of actual production. In recent years, deep learning methods have shown excellent performance in fruit target recognition and localization tasks. The fruit target recognition algorithm based on deep learning has the advantages of high detection progress and fast detection speed, so it is widely used in the fruit target recognition task under different scenes and has achieved many good achievements. The data set preparation and fruit target recognition models were reviewed. Firstly, the characteristics of supervised, semi-supervised and unsupervised methods related to dataset preparation were summarized. Secondly, according to the development process of deep learning algorithm, the common methods and practical applications of deep learning-based fruit target detection and segmentation technology were summarized, the previous research on the detection and segmentation of fruit objects such as apple, citrus and tomato under different natural scenes was summarized, and the research progress and application of lightweight model were summarized. Thirdly, the problems and challenges of deep learning-based fruit target recognition technology were summarized. In the end, the future development trend of deep learning-based fruit target recognition methods was pointed out, that was, weakly supervised learning would be used to reduce the dependence of models on data labels, and the detection speed of lightweight models would be improved to achieve real-time and accurate detection of fruit targets.