Stolon-YOLO: A detecting method for stolon of strawberry seedling in glass greenhouse

Stolons are an essential nutritional organ of strawberry seedlings, whose quantity and vigorous growth directly impact the quality of the seedling cultivation. Accurately detecting stolon is essential for increasing productivity in seedling factories. To alleviate these issues of low recognition accuracy of stolon caused by complex backgrounds, diverse growth types and low proportion of effective information in the bounding box, we propose a Stolon-YOLO for visual recognition of stolon for the first time. In our method, improvements are focused on two aspects: HorBlock-decoupled head and Stem Block feature enhancement module. Firstly, a new decoupled head is designed by introducing the HorBlock, which enables the interaction of high-order spatial information and enhances the precision for locating stolons. Sequential gnConv of the HorBlock realizes better global modeling for incomplete and curved stolon to improve detection efficiency. Then, the Stem Block is used to reinforce feature expression and reduce dimensionality. We fuse the processing results of the Stem Block with the output of HorBlock in a residual form. Experimental results demonstrate that the Stolon-YOLO achieves 92.5% in precision, 89.7% in recall rate, 91.1% in F1 score and 88.5% in average precision for stolon detection, which outperforms the standard YOLOv7 by 3% in recall rate and 3.4% in average precision. Compared with YOLOX, SSD, CenterNet and Faster R-CNN, the Stolon-YOLO increases recall rate by 8.3%, 27.4%, 17%, and 3.5%, and increases AP by 12.2%, 28.9%, 2.4%, and 7.5%, respectively. Meanwhile, the Stolon-YOLO achieves a frame rate of 50.25 FPS, meeting real-time detection demands. The above results highlight that this study provides a fast and effective method for identifying the stolon of strawberry seedlings in glass greenhouses.