Distilling Knowledge to Efficient Transformer for Semi-Supervised Citrus Maturity Detection using Consumer UAVs

Accurate detection of citrus fruit maturity is critical for optimizing harvest schedules and maximizing yield. Consumer-grade unmanned aerial vehicles (UAVs) have emerged as cost-effective alternatives to traditional methods for detecting maturity, which rely on labor-intensive manual inspections. This paper presents a two-step, semi-supervised approach leveraging knowledge distillation (KD) and transfer learning for citrus maturity detection in UAV images. Specifically, we combine teacher-filtered pseudo-labels with a consistency-guided feature distillation signal to exploit abundant unlabeled UAV frames while using only a small labeled seed set. Firstly, a consistency-guided KD transfers knowledge from a pretrained detection transformer with collaborative hybrid assignment training (Co-DETR) to a lightweight student network by exploiting a small labeled and a large unlabeled dataset. The student network (Cit-DETR) is based on the highly efficient detection transformer (RT-DETR) having a ResNet18 backbone with selective kernel blocks and the hybrid encoder module. Step 2 uses a small labeled augmented dataset with maturity labels to fine-tune the Cit-DETR model for maturity detection. Experimental results on a custom UAV-captured citrus dataset demonstrate the effectiveness of our method, achieving 86.2% average precision in citrus detection and 91.0% mean average precision in ripeness detection. The model has been further optimized for real-time inference on edge devices or UAVs, enabling precision agriculture applications.