BiFormer-Enhanced YOLOv11n for Accurate Maize Ear Detection in Seed Production Fields

Accurate identification of maize ears during harvesting is a prerequisite for enabling automated operations and yield estimation in the seed maize industry. Utilizing computer vision algorithms to assist seed maize harvesters in adjusting operating conditions in real time can significantly enhance harvesting quality. To address existing challenges in ear recognition during active harvesting—such as large variations in individual ear sizes, complex field environments, and difficulties in dynamic scene perception—this paper proposes an improved model, BiF-YOLO, to achieve precise recognition of seed maize ears during the harvest season. This model is based on the YOLOv11n model and the BiFormer architecture. Three targeted enhancement strategies are proposed to optimize the model; (1) to enhance the recognition capability for occluded and small-sized maize ears in the field, a C3k2_AdditiveBlock module is embedded into the feature extraction network; (2) to suppress background interference during recognition, focus on the maize ear regions, and reduce missed and false detections, a C2BRA dual-layer routing attention mechanism (Cross-stage partial 2 Bottleneck with Residual Attention) is established; (3) the ShapeIoU loss function is employed to improve the model’s attention to the shape characteristics of maize ears themselves. Seed maize ear images were collected from seed production fields in Jiaozhou and Zhangye. The experimental results validated that the BiF-YOLO model effectively adapts to complex in-field operational scenarios.