YOLOv8s-SLS: a mulberry leaves pest detection model integrating lightweight and multiscale

In order to reduce the economic losses caused by pest infestation during mulberry leaf cultivation, this paper proposes a YOLOv8 detection model integrating lightweight and multi-scale, named YOLOv8s-SLS. The channel-to-feature-to-space channel (C2FSC) module is first introduced in the backbone network to compensate for feature information lost due to model deepening by using complementary information between neighboring regions. Then, the neck structure and the detector head (NLN) were redesigned to improve the recognition of target pests at multiple scales while removing redundant connections in the model. Finally, the LSKA module enhanced the feature representation of the model by dynamically adapting to the sensory field. In addition, a mulberry leaf pest dataset containing different target sizes, named MPD1, consisting of 1705 raw images of three pests, was constructed for model training and validation. The experimental results on the test dataset showed that the parameters of the enhanced and multi-scale versions of the model were reduced by about 15% and the mAP50 was improved by 3.7% compared with the original YOLOv8 model. The experiments proved that the model can quickly and accurately identify pests in mulberry gardens, providing feasible technical support for real-time detection of pests in the sericulture industry.