## 1 Introduction
[PERSON] et al. described the difficulties of fruit recognition and location: target identification in the scene of occlusion and light changes environments, target tracking in dynamic jamming environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots [12]. The strawberry robot designed by [PERSON] et al.,was equipped with internal sensors where the gripper could sense and correct the position error; it had a robustness to positioning error introduced by the vision module. The robot can select isolated strawberries with a near perfect success rate (96.8%) [13]. Some scholars use the YOLO algorithm to detect and identify fruits. [PERSON] et al. proposed a panoramic method of viewing the full fruit yield and counting based on deep learning object detection; the researchers verified this via holly trees with dense fruits. Accurate fruit counting has good robustness to shadows, coverage, and incomplete contours [14]. [PERSON] et al. raised an apple target detection method based on Shuffllenetv2-YOLOX. The _precision, recall, average precision_, and _F1_ of the trained network under the verification set are 95.62%, 93.75%, 96.76%, and 0.95, respectively, and the detection speed reaches 65 frames per second [15]. [PERSON] et al. proposed the DSE-YOLO to detect multi-stage strawberries, with the _mean average precision_ value of 86.58% and _F1_ score value of 81.59% [16]. [PERSON] et al. raised an improved YOLO-V3 model to detect apples at different growth stages in gardens with a fluctuating illumination, sophisticated background, and overlapping apples, leaves, and branches. The results demonstrated that the model has an average detection time of 0.304 s per frame at a resolution of 3000 \(\times\) 3000 [17]. [PERSON] et al. compared the performance of Faster R-CNN (VGG) and Faster R-CNN (ZF) and single-stage technologies of YOLOv2, YOLOv2 (tiny), YOLOv3 and SSD in detecting mango fruits in tree crown images. Based on YOLOv2 (tiny) and YOLOv3, a new architecture \"MangoYOLO\" has also been developed [18]. [PERSON] et al. established an automatic counting method based on deep learning and trunk tracking. The _mean average precision_ (_mAP_) of the trunk and fruit detection was 99.35%. In an orchard video, the counting accuracy of the manual counting method was 91.49%, and the correlation coefficient R\({}^{2}\) was 0.9875, with the counting performed by manual counting [19]. [PERSON] et al. used the object detection model YOLOv4 and the multi object tracking algorithm depth SORT mobile application to generate a powerful real-time pear fruit counter [20]. [PERSON] et al. used the YOLO detection model to detect and count oranges. The _mean average precision_ (_mAP_), _precision_, _recall_, and _F1_ score of YOLOv4 as the best model for orange detection were 90.8%, 91.23%, 92.8%, and 92%, respectively [21]. [PERSON] et al. developed a deep learning artificial neural network model of wild blueberry at maturity, and the model was used for yield estimation [22]. [PERSON] et al. improved the YOLOv4 tiny model and detected the fruits under various lighting conditions. Although the lighting changes were significant, the algorithm still shows a high positioning stability and robustness [23]. [PERSON] et al. used the improved YOLOv5 depth learning algorithm to put forward a visual recognition method for apple growth morphology in the orchard. The _mean average precision_ (_mAP_) reached 98.4%, and the _F1_ score was 0.928 [24]. [PERSON] et al. proposed a precise apple fruit detection method with a small model size based on the channel pruning YOLOv5s algorithm. The _recall_ rate, _precision_, _F1_-score and false detection rate were 87.6%, 95.8%, 91.5%, and 4.2%, respectively [25]. [PERSON] et al. proposed a lightweight apple target detection method for picking robots using the improved YOLOv5s. The improved network model can effectively identify graspable apples that are not occluded or only occluded by leaves and non-graspable apples that are occluded by branches or other fruits.