STRAWBERRY IDENTIFICATION AND KEY POINTS DETECTION FOR PICKING BASED ON IMPROVED YOLOV8-POSE AT RED RIPE STAGE

To solve the problems of low precision in locating stem picking points and difficulty in recognizing occluded strawberry during the operation of strawberry picking robots, this paper proposed an improved YOLOv8-pose method for strawberry fruits identification and key points detection at the red ripe stage. Based on the YOLOv8-pose human posture estimation model, three categories (strawberry, stem, and picking points) were annotated. The acquired images were divided into training, validation, and test sets in an 8:1:1 ratio. In order to improve the feature extraction ability of the model for small targets, shuffle attention (SA) mechanism was added into the backbone network of YOLOv8-pose. Additionally, a comparative analysis was conducted to assess the impact of six attention mechanisms of CBAM (Convolutional block attention module), SimAM (Simple attention module), GAM (Global attention module), EMA (Efficient multi-scale attention), SK (Selective kernel attention), and SA on the detection results. Experimental results show that the proposed method can quickly and accurately detect strawberry fruits and key points for picking. The Precision (P), Recall (R), and mean average precision (mAP)50 values for both bounding boxes and key points based on SA mechanism were 99.7%, 100.0%, and 99.5% respectively, which were superior to the other attention mechanisms. Compared with YOLOv5-pose and YOLOv8-pose models, the improved model had the best P, R, and mAP50 values, and its memory usage was 6.4MB, which was also optimal. The improved method can provide crucial technical support for precise robotic strawberry picking.