A UAV-Based Method for Detecting and Geolocating Missed Tassels in Hybrid Maize Seed Production

Abstract

Maize tassel detection is essential for maintaining genetic purity in hybrid seed production. However, existing methods often show limited performance in detecting small or occluded missed tassels and are difficult to deploy efficiently on resource-constrained field devices. To address these challenges, this study proposes MTDP-YOLO, a lightweight detection framework based on YOLOv11 for UAV-based missed tassel detection. The proposed framework integrates an HGNet backbone for efficient feature extraction, a BiFPN-GLSA dual-path feature fusion structure combined with FEFM to enhance multi-scale representation and small-target discrimination, and a layer-adaptive pruning strategy with channel-wise knowledge distillation to further compress the model while preserving accuracy. In addition, a dedicated in-domain UAV dataset containing 9,146 images after training-set augmentation was constructed to represent diverse field conditions, including complex backgrounds, illumination variation, and target occlusion. Experimental results show that MTDP-YOLO achieves 89.5% mAP@0.5 with only 1.6 M parameters and 5.8 GFLOPs, representing a 1.1-percentage-point improvement over YOLOv11n while reducing computational cost. After pruning and distillation, the compressed model maintains competitive detection performance and supports efficient offline inference on portable ground devices. Furthermore, a geographic coordinate extraction method was developed and integrated into a custom Missed Tassel Detection and Geolocation Software, enabling automatic conversion of detection results into georeferenced target locations. Field validation provided initial evidence of sub-meter positioning feasibility under the evaluated conditions, indicating its potential for practical post-detasseling inspection and precision field management in hybrid maize seed production.