The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. To address these challenges, we propose DHS-YOLO, a novel deep learning framework optimized for robust wheat seedling detection under diverse illumination intensities. Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. Second, the Histogram Transformer (HT) module integrates a dynamic-range spatial attention mechanism to mitigate illumination-induced image degradation. Third, we implement the ShapeIoU loss function that prioritizes geometric consistency between predicted and ground truth bounding boxes, particularly optimizing for slender plant structures. The experimental validation was conducted using a custom UAV-captured dataset containing wheat seedling images under varying illumination conditions. Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. These results demonstrate our model’s effectiveness in overcoming illumination variations while maintaining high sensitivity to fine plant structures. This research contributes an optimized computer vision solution for precision agriculture applications, particularly enabling automated field management systems through reliable crop detection in challenging environmental conditions.