CSGD-YOLO: A Corn Seed Germination Status Detection Model Based on YOLOv8n

作者：Wenbin Sun, Ming Xu, Kang Xu, Dongquan Chen, Jianhua Wang, Ranbing Yang, Quanquan Chen, Songmei Yang · 发表于：Agronomy · 年份：2025 · DOI：10.3390/agronomy15010128 · 被引用次数：17 · 研究领域：Smart Agriculture and AI

Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP0.5, and mAP0.50:0.95...