Seedling-DETR: A Detection Transformer Model for Maize Seedling Monitoring Using Multispectral UAV Images

Maize is a globally important staple crop, and automated monitoring of germination and seedling emergence is essential for precision agriculture, enabling timely reseeding and reducing potential yield loss. To address this need, we propose Seedling-DETR, a transformer-based model for the real-time detection of emerged and missing maize seedlings using multispectral UAV imagery in an end-to-end manner. First, we construct a multispectral UAV dataset and ntroduce a dedicated annotation strategy in which missing seedlings were labeled individually rather than inferred indirectly. Then, we modify the feature fusion module of RT-DETR and develop a hybrid-scale feature fusion module to obtain richer and more expressive feature representations for missing seedling detection and improve the precision of missing seedling detection. Finally, we propose a channel fusion module to incorporate multispectral images into our model without requiring a dedicated multispectral backbone or additional pretraining, thereby improving model adaptability. The results show that, under a random train–test split (8:2), when using RGB images as input, our Seedling-DETR achieves a mean average precision (mAP) of 83.1% at an IoU threshold of 0.5, outperforming YOLOv11x and RT-DETR by 2.5% and 1.1%, respectively. The proposed method achieves an AP of 69.3% at an IoU threshold of 0.5 for missing seedling detection, which increases to 71.7% when multispectral inputs are incorporated. Similar performance trends are observed on an independent validation set collected on a different date. Although the model introduces moderate computational overhead (282 GFLOPs for RGB input and 418 GFLOPs for multispectral configuration, with 84.0 M and 85.1 M parameters, respectively), it can maintain efficient detection performance suitable for actual agricultural field deployment. The method is further validated at the field scale using orthomosaic-based analysis. Overall, this study provides an effective and scalable framework for the detection of emerged and missing maize seedlings under complex field conditions. The proposed framework supports accurate reseeding decisions, and contributes to automated maize production.