Abstract
A lightweight seedling detection model with improved YOLOv8s is proposed to address the seedling identification problem in the replenishment process In industrial vegetable seedling production, First, the CBS module in the network structure is replaced with depthwise separable convolution (DSC) to reduce the number of parameters and GFLOPS of the model. The efficient multiscale attention (EMA) module is added to the structure to improve the feature extraction capability of the network, focusing on the target regions of empty and unqualified seedlings in seedling trays in complex environments. Second, the VoVGSCSP module is utilized to replace the C2f module in Neck to further lighten the model and improve its accuracy. Compared with the original YOLOv8s model, the Precision, Recall, and mAP of the improved model on the test set are 95.9%, 91.6%, and 96.2%, respectively, and its parameters, GFLOPS, and model size are 7.88 M, 20.9, and 16.1 MB, respectively. The detection speed of the algorithm is 116.3 frames per second (FPS), which is higher than that of the original model (107.5 FPS). The results indicate that the improved model can accurately identify empty cell and unqualified seedling in the plug tray in real time and has a smaller number of parameters and GFLOPS, making it suitable for use on embedded or mobile devices for seedling replenishment and contributing to the realization of automated and unmanned seedling replenishment.