Citrus Huanglongbing (HLB) is one of the most destructive diseases in the global citrus industry; its pathogen is transmitted primarily by the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, making timely monitoring and control of ACP populations essential. Real-world ACP monitoring faces several challenges, including tiny targets easily confused with the background, noise amplification and spurious detections caused by textures, stains, and specular glare on yellow-boards, unstable localization due to minute shifts of small boxes, and strict constraints on parameters, computation, and model size for long-term edge deployment. To address these challenges, we focus on the yellow-board ACP monitoring scenario and create the ACP Yellow Sticky Trap Dataset (ACP-YSTD), which standardizes background and acquisition procedures, covering common interference sources. The dataset consists of 600 images with 3837 annotated ACP, serving as a unified basis for training and evaluation. On the modeling side, we propose TGSP-YOLO11, an improved YOLO11-based detector: the detection head is reconfigured to the two scales P2 + P3 to match tiny targets and reduce redundant paths; Guided Scalar Fusion (GSF) is introduced on the high-resolution branch to perform constrained, lightweight scalar fusion that suppresses noise amplification; ShapeIoU is adopted for bounding-box regression to enhance shape characterization and alignment robustness for small objects; and Network Slimming is employed for channel-level structured pruning, markedly reducing parameters, FLOPs, and model size to satisfy edge deployment, without degrading detection performance. Experiments show that on the ACP-YSTD test set, TGSP-YOLO11 achieves precision 92.4%, recall 95.5%, and F1 93.9, with 392,591 parameters, a model size of 1.4 MB, and 6.0 GFLOPs; relative to YOLO11n, recall increases by 4.6%, F1 by 2.4, and precision by 0.2%, while the parameter count, model size, and computation decrease by 84.8%, 74.5%, and 4.8%, respectively. Compared to representative detectors (SSD, RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLOv12n, YOLOv13n), TGSP-YOLO11 improves recall by 33.9%, 19.0%, 8.5%, 10.1%, 6.3%, 4.6%, 6.9%, and 5.7%, respectively, and F1 by 19.9, 14.9, 5.1, 6.0, 2.6, 5.6, 3.6, and 3.9, respectively. Additionally, it reduces parameter count, model size, and computation by 84.0–98.8%, 74.5–97.9%, and 3.2–94.2%, respectively. Transfer evaluation indicates that on 20 independent yellow-board images not seen during training, the model attains precision 94.3%, recall 95.8%, F1 95.0, and 159.2 FPS.

This study proposes an adaptive broad learning strategy for a two-degree-of-freedom helicopter system based on specified performance and dynamic event-triggered. First, broad learning is employed to approximate system uncertainties. Compared to radial basis function neural networks, broad learning achieves this by increasing the number of nodes, enabling it to approximate system uncertainties with smaller tracking errors. At the same time, an error transformation method is employed to guarantee that the tracking error adheres to a predefined performance function. Furthermore, a dynamic event-triggering mechanism reduces communication overhead and prevents the Zeno effect. Subsequent Lyapunov-based stability analysis confirms that the system exhibits semi-global consistency, stability, and boundedness. In addition, simulation results verify the proposed control strategy’s effectiveness and robustness.