As food security gains increasing attention, automated pest monitoring is crucial for agricultural early warning systems. However, in practical light-trap capturing sensors, the extremely small scale of pests and complex background interference, such as unexpected reflection and occlusions, severely undermine the performance of existing models, resulting in frequent missed and false detections. To deal with these challenges, this study proposes EP-YOLO, an enhanced lightweight detection architecture based on YOLOv8n. Specifically, to retain the spatial pixels of micro-targets during downsampling and isolate pest features while eliminating background noise without compromising channel information, the Spatial-to-Depth Convolution (SPD) module and the Efficient Multi-Scale Attention (EMA) module are introduced. We evaluate our model through experiments on Pest24, a dataset consisting of 24 tiny pest categories. The results demonstrate that EP-YOLO achieves a mAP@50 and mAP@50:95 of 70.5% and 47.3%, respectively, improving upon the baseline by 1.1% and 1.9%. Furthermore, EP-YOLO achieves a significant improvement in detecting certain extremely small pests. For example, Rice planthopper and Plutella xylostella show improvements of 8.4% and 3.1%, respectively, compared to the baseline. In conclusion, the physical limitations of detecting tiny pests are successfully overcome by EP-YOLO, providing a robust and deployable design for real-time agricultural monitoring systems.

Text-based automatic compliance checking (ACC) employs natural language processing technologies to scrutinize a corporation’s business documents, ensuring adherence to related normative texts. The current methods fall into two primary categories: symbol-based and embedding-based approaches. Symbol-based methods, noted for their accuracy and transparent processing, suffer from limited versatility. Conversely, embedding-based methods operate independently of expert knowledge yet often yield challenging-to-interpret results and require substantial volumes of annotated data. While both types of methods exhibit advantages in different aspects, the current research fails to combine these advantages effectively. Therefore, the existing methods fail to balance interpretability, generalization ability, and accuracy, which are key requirements for practical compliance systems. To address this problem, we introduce a novel approach termed the Dynamic Prompt-based Logic-Aided Compliance Checker (DPLACC), which is grounded in the prompt learning framework. This method initially parses target texts, transforming the results into first-order logical expressions. It subsequently retrieves pertinent knowledge from a knowledge graph, converting the knowledge into analogous first-order logical expressions. These expressions are then encoded into a global semantic vector via a pre-trained first-order logistic encoder. Ultimately, the semantics of expressions and initial texts are amalgamated within the prompt template, facilitating the logical knowledge enhancement of model reasoning. Experiments on Chinese and English datasets demonstrate that DPLACC comprehensively outperforms existing methods based solely on symbols or embeddings in terms of accuracy, precision, recall, and F1 score and significantly surpasses current mainstream large language models. Furthermore, DPLACC exhibits enhanced interpretability and reduced data dependence, maintaining 70% checking accuracy with as few as ten training samples. This capability allows DPLACC to be rapidly deployed in data-scarce real-world scenarios with minimal annotation overhead, thus offering a practical pathway toward the scalable implementation of compliance inspection systems.