KD-SSGD: detección de germinación semi-supervisada mejorada por destilación de conocimiento

With the rapid development of precision agriculture, seed germination detection is crucial for crop monitoring and variety selection. Existing fully supervised detection methods rely on large-scale annotated datasets, which are costly and time-intensive to obtain in agricultural scenarios. To tackle this issue, we introduce a knowledge distillation-enhanced semi-supervised germination detection framework (KD-SSGD) that requires no pre-trained teacher and supports end-to-end training. Built on a teacher-student architecture, KD-SSGD introduces a lightweight distilled student branch and three key modules: Weighted Boxes Fusion (WBF) to optimize pseudo-label localization, Feature Distillation Loss (FDL) for deep semantic knowledge transfer, and Branch-Adaptive Weighting (BAW) to stabilize multi-branch training. On the Maize-Germ (MG) open-access dataset, KD-SSGD achieves 47.0% mAP with only 1% labeled data, outperforming Faster R-CNN (35.6%), Mean Teacher (41.9%), Soft Teacher (45.1%), and Dense Teacher (45.0%), and reaches 59.3%, 62.8%, and 65.1% mAP at 2%, 5%, and 10% labeled ratios. On the Three Grain Crop (TGC) open-access dataset, which achieves 73.3%, 75.3%, 75.6%, and 76.1% mAP at 1%, 2%, 5%, and 10% labels, surpassing mainstream semi-supervised methods and demonstrating robust cross-crop generalization. The results indicate that KD-SSGD could generate high-quality pseudo-labels, effectively transfer deep knowledge, and achieve stable high-precision detection under limited supervision, providing an efficient and scalable solution for intelligent agricultural perception.