Lightweight Detection of Blueberries at Different Maturity Stages in Complex Orchard Environments

Accurate detection of blueberries at different maturity stages supports orchard monitoring and harvest planning, but remains difficult under complex field conditions because fruits are small, densely clustered, frequently occluded, and show subtle colour transitions across immature, colour-turning, and ripe stages. A key technical challenge for lightweight detectors is to preserve fine spatial information for small fruits while maintaining sufficient contextual representation for dense clusters and visually ambiguous maturity stages without substantially increasing inference complexity. To address this gap, this study develops a lightweight detection framework that jointly targets small-object perception, contextual feature enhancement, and training-stage semantic supervision. A field dataset containing 4680 images and 60,573 ground-truth bounding boxes was constructed. The P3/P4/P5 detection structure of YOLOv12n was redesigned as a P2/P3/P4 architecture to strengthen high-resolution feature representation for small objects. A lightweight feature-enhancement module based on single-head self-attention, termed SHSA2C2f, was introduced to improve contextual representation in dense fruit clusters and visually ambiguous maturity stages. DINOv3 (ViT-S/16)-guided asymmetric knowledge distillation was further evaluated as a training-only semantic supervision strategy that introduces no additional inference cost. Across three independent runs, the accuracy-oriented M4 model achieved 92.68 ± 0.04% [email protected] and 86.38 ± 0.10% [email protected]:0.95 with 0.79 M parameters, while its colour-turning AP50 reached 89.78 ± 0.28%. For the same student architecture, introducing the asymmetric distillation strategy increased recall from 85.00 ± 0.52% to 85.81 ± 1.12%, while slightly reducing [email protected]:0.95 from 86.38 ± 0.10% to 86.19 ± 0.17%, indicating a recall-oriented performance trade-off. After TensorRT FP16 conversion, M4 retained 92.75% [email protected] and 86.24% [email protected]:0.95 and ran at 15.9 FPS on a Jetson Orin Nano, supporting its feasibility for edge-device inference under the evaluated conditions.