番茄钵苗分类识别对自动移栽机精准和高效完成钵苗移栽作业具有重要意义，目前钵苗检测存在精度较低，检测实时性较差，有误检和漏检的问题。为建立番茄钵苗监测系统，保证检测实时性，提高钵苗识别精准度，基于改进YOLOv5s目标检测算法提出一种番茄钵苗分类识别模型。研究工作包括构建番茄钵苗数据集；引入全局注意力机制（global attention mechanism, GAM）注意力机制；采用动态非单调聚焦机制损失函数（wise IoU, WIOU）损失函数策略；运用可变形卷积DCN V3；采用上下文信息模块（context augmentation module, CAM）模块。改进后模型平均检测速度约为12 ms，平均精度（average precision, AP）较基础模型上升3.8个百分点，MAP@0.5提高1.9个百分点，召回率提升3.2个百分点。相同实验条件下，将改进后YOLOv5s模型与当下常用模型对比，其检测速度更快，符合钵苗检测要求，精度更高，总体效果更优，保证实时性的基础上有效提高番茄钵苗的识别精度。

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.