Yanxi Liu

Ultraviolet (UV) insect traps enable continuous field pest monitoring, but their images contain severe scale variation, dense and overlapping targets, incomplete specimens, background stains, insect fragments, non-target insects, inter-class similarity, and long-tailed distributions, leading to missed detections, false positives, and class confusion. To address these challenges, we constructed a real-world UV insect-trap pest dataset comprising 12,426 high-resolution images and 38,003 annotated instances from 36 pest categories, and propose FCD-DETR, a foreground-aware and context-enhanced Detection Transformer based on RT-DETR-R18. The main distinction of FCD-DETR lies in a progressive enhancement design tailored to UV insect-trap images, where FADM first reduces foreground–background confusion by decoupling pest foreground cues from multi-scale semantic information, C2f_CFBlock strengthens local-detail and contextual representations in the lightweight backbone, and DHSA further alleviates high-level semantic ambiguity through dynamic-range histogram self-attention in the Transformer encoder. On the proposed dataset, FCD-DETR achieved 64.42% mAP@0.5 and 41.33% mAP@50:95, improving the baseline by 5.25 and 5.11 percentage points, respectively, and outperforming representative CNN- and Transformer-based detectors. Comprehensive experimental analyses confirm that FCD-DETR improves foreground discrimination and detection robustness in complex UV insect-trap scenarios.

Maize (Zea mays L.), a vital global food crop, relies on its stomatal structure for regulating photosynthesis and responding to drought. Conventional manual stomatal detection methods are inefficient, subjective, and inadequate for high-throughput plant phenotyping research. To address this, we curated a dataset of over 1500 maize leaf epidermal stomata images and developed a novel lightweight detection model, StomaYOLO, tailored for small stomatal targets and subtle features in microscopic images. Leveraging the YOLOv11 framework, StomaYOLO integrates the Small Object Detection layer P2, the dynamic convolution module, and exploits large-scale epidermal cell features to enhance stomatal recognition through auxiliary training. Our model achieved a remarkable 91.8% mean average precision (mAP) and 98.5% precision, surpassing numerous mainstream detection models while maintaining computational efficiency. Ablation and comparative analyses demonstrated that the Small Object Detection layer, dynamic convolutional module, multi-task training, and knowledge distillation strategies substantially enhanced detection performance. Integrating all four strategies yielded a nearly 9% mAP improvement over the baseline model, with computational complexity under 8.4 GFLOPS. Our findings underscore the superior detection capabilities of StomaYOLO compared to existing methods, offering a cost-effective solution that is suitable for practical implementation. This study presents a valuable tool for maize stomatal phenotyping, supporting crop breeding and smart agriculture advancements.