YOLOv11-LicoSeg: A Method for Measuring the Radicle Length of Licorice

Global climate change and soil salinization pose challenges to licorice cultivation. Evaluating seed vigor based on the dynamic changes in radicle morphology is crucial for screening and cultivating licorice varieties that are tolerant to low temperatures and salts. Traditional manual measurement of licorice radicle characteristics suffers from issues such as high cost, long time consumption, and large errors. The YOLOv11 instance segmentation model in the field of deep learning offers advantages including a simple architecture, strong lightweight properties, and a unified detection-segmentation framework. Therefore, this study selected the YOLOv11 model to build a deep learning framework and used the continuous time-series crop growth vitality monitoring system to collect full-time-series images of 18 groups of licorice seeds germinating under different temperature and salt stress conditions. The YOLOv11-seg model was improved by adding a Spatial Strip Attention mechanism (SSA) to enhance the spatial correlation of radicle features, replacing ordinary convolutions with a Multi-scale Edge Detail Enhancement Module (MEEM) to optimize multi-scale feature extraction capabilities, and embedding a Normalized Weighted Distance (NWD) loss function to strengthen the segmentation ability for tiny targets. The YOLOv11-LicoSeg model was constructed for segmenting and extracting licorice radicle features and calculating root length. The experimental results showed that the mAP50 of the model’s detection reached 97.4%, mAP50–95 reached 81.7%, the mAP50 of the segmentation mask reached 97.0%, and mAP50–95 reached 78.2%. Compared with the unimproved YOLOv11-seg, the mAP50 of detection increased by 0.7%, mAP50–95 increased by 1.3%, the mAP50 of segmentation increased by 0.7%, and mAP50–95 increased by 0.8%. The linear regression coefficient between manual measurement and machine-vision measurement was 0.94218, and the goodness of fit R2 was 0.94408. Using this model and the monitoring system, the morphological evolution of the licorice radicle contour characteristics over the germination time was obtained. The study indicated that the growth of licorice radicles was optimal under salt stress of 1200 µs/cm and 1800 µs/cm. YOLOv11-LicoSeg accurately segmented licorice radicles and calculated radicle length, with the performance to segment 100 licorice radicle images within 7 s. After deployment, it significantly reduced the labor cost and time consumption for acquiring licorice radicle phenotypes. In conclusion, YOLOv11-LicoSeg provides a rapid and accurate method for variety screening in licorice breeding and cultivation.