AUTHOR=Gao Sheng , Cui Gongpei , Wang Qiaohua 

TITLE=WCS-YOLOv8s: an improved YOLOv8s model for target identification and localization throughout the strawberry growth process

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1579335

DOI=10.3389/fpls.2025.1579335

ISSN=1664-462X

ABSTRACT=IntroductionTo enhance the quality and yield of strawberries, it is essential to effectively supervise the entire growing process. Currently, the monitoring of strawberry growth primarily relies on manual identification and positioning methods. This approach presents several challenges, including low efficiency, high labor intensity, time consumption, elevated costs, and a lack of standardized monitoring protocols. On the basis of this, there was an urgent need in the market to automate the whole process of target recognition and localization in strawberry growing.MethodsAiming at the above problems, we innovatively constructed a model for target recognition and localization of strawberries based on the YOLOv8s benchmark model, named the WCS-YOLOv8s model. In this paper, the whole growth process of the strawberry was divided into four stages, namely, the bud, flower, fruit under-ripening, and fruit ripening stages, and a total of 1,957 images of these four stages were captured with a binocular depth camera. Using the constructed WCS-YOLOv8s model to process the images, the target recognition and localization of the whole growth process of the strawberry were accomplished. This model proposes a data enhancement strategy based on the Warmup learning rate to stabilize the initial training process. The self- developed SE-MSDWA module is integrated into the backbone network to improve the model’s feature extraction capability while suppressing redundant information, thereby achieving efficient feature extraction. Additionally, the neck network is enhanced by incorporating the CGFM module, which employs a multi-head self-attention mechanism to fuse diverse feature information and improve the network’s feature fusion performance.Results and discussionThe model’s Precision (P), Recall (R),  HYPERLINK "mailto:mAP@0.5" mAP@0.5, and mAP@0.5:0.95 of detection were 83.4%, 86.7%, 87.53%, and 60.48%, respectively, and the detection speed was 45.9 FPS(21.8 ms/per image, which significantly improved on the detection accuracy and generalization ability of with the YOLOv8s benchmark model. This model can meet the demand for online real-time target identification and localization of strawberries and provide a new detection method for the automated monitoring and management of the whole growth process of strawberries.