Accurate tomato potting seedling classification and identification are crucial for automatic transplanting machines to efficiently carry out potting seedling transplant operations. However, the current potting seedling detection suffers from low precision, inadequate real-time detection, and issues of false detection and omissions. To develop a tomato potting seedling monitoring system ensuring real-time detection and enhancing potting seedling identification accuracy, a tomato potting seedling classification and identification model was proposed based on enhancements to the YOLOv5s target detection algorithm. The research involved creating a tomato potting seedling dataset, incorporating the GAM(global attention mechanism) attention mechanism, implementing the WIOU(wise IoU) loss function strategy, utilizing the deformable convolutional DCN V3, and integrating the CAM(context augmentation module) module. The enhanced model achieved an average detection speed of approximately 12 ms, with the average accuracy AP(average precision) increasing by 3.8 percentage points compared to the base model, MAP@0.5 rising by 1.9 percentage points, and R improving by 3.2 percentage points. When compared under identical experimental conditions, the improved YOLOv5s model exhibits faster detection speed than the commonly used contemporary model, meeting potting seedling detection requirements with enhanced accuracy and improved overall performance, thereby ensuring real-time detection through effective enhancement of tomato potting seedling recognition accuracy.