Efficient detection and counting method for maize seedling plots

• We proposed an image enhancement method for maize seedlings in field scenarios, named "M_AUG.". • We developed an efficient detection method for maize seedlings that is applicable under various viewing angles. • This method demonstrates the best overall performance when compared with mainstream methods. • The method was deployed on an edge platform, and tests for detection and counting in different plots were conducted, validating its accuracy and adaptability. To efficiently detect and count maize seedlings in complex field conditions, this study first developed a sample dataset under diverse backgrounds and lighting scenarios and introduced a data augmentation technique called “M_AUG.” YOLOv5s was selected as the base model, enhanced with the Swin Transformer (Swin TR)to improve feature extraction across various scales and complex environments. The model also incorporated multi-scale attention (EMA)to enhance the representation of small samples and positive/negative samples, along with the Asymptotic Feature Pyramid Network (AFPN)to integrate seedling features at different levels. The results showed that the proposed SEA-YOLOv5 achieved mAP 0.5 of 98.6 %, mAP 0.5–0.95 of 73.2 %. and F1 of 97.1 %, with the parameters count of 5.55 million and a weight size of 11.7 MB. Compared to YOLOv5, SEA-YOLOv5 improved mAP 0.5 by 5.8 %, mAP 0.5–0.95 by 9.9 %, and F1 by 5.4 %, while reducing the parameter count by 1.46 million and weight size by 2.7 MB. SEA-YOLOv5 was compared with YOLOv7, YOLOv8s, Faster R-CNN, RetinaNet, YOLOv10s, DNE-YOLO, and YOLOv11s, and the results indicated that SEA-YOLOv5 outperformed the comparison models in overall performance. Upon deploying SEA-YOLOv5 on the Jetson Orin NX and conducting seedling detection and counting trials across eight plots, the model achieved a miss rate of just 0.63 % and a frame rate of 74.6 FPS. Thus, it can be concluded that the SEA-YOLOv5 model developed in this study provides high accuracy, a compact design, and strong portability, making it well-suited for real-time detection and counting applications in the field.