To 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 localisation in strawberries. Aiming at the above problems, based on the YOLOv8s benchmark model, this paper innovatively constructed a model for target recognition and localisation of strawberries, the WCS-YOLOv8s model. In this paper, the whole growth process of strawberry was divided into four stages, namely: bud stage, flower stage, fruit under-ripening stage, and fruit ripening stage, and a total of 1957 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 localisation 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 multihead self-attention mechanism to fuse diverse feature information and improve the network's feature fusion performance.
Gao, S., Cui, G., & Wang, Q. WCS-YOLOv8s: Improved YOLOv8s model for target identification and localisation throughout the strawberry growth process. Frontiers in Plant Science, 16, 1579335. https://doi.org/10.3389/fpls.2025.1579335
Source: Frontiers In