Liangquan Jia

China’s publishing industry continues to expand across production, warehousing, circulation, and export. Book quality inspection affects product grading, logistics sorting, customer satisfaction, and the international image of Chinese publishing. However, these defects are often small, irregular, and low-contrast, and their visual similarity to paper textures and printed content complicates automated classification and localization. To address this problem, this study proposes Local-detail Modelling and Edge-enhancement YOLO (LME-YOLO), a book defect detection model based on YOLO11n for quality inspection in China’s publishing logistics. On a dataset of 632 book images, LME-YOLO achieved a Precision of 0.829, Recall of 0.777, mAP50 of 0.818, and mAP50–95 of 0.465. The model contains 2.64 M parameters and requires 6.6 GFLOPs. Among the detectors compared in this study, LME-YOLO achieved the highest mAP50 and mAP50–95. Compared with YOLO11n, its mAP50 and mAP50–95 increased by 0.015 and 0.006, respectively, with increases of 0.05 M parameters and 0.2 GFLOPs. Compared with YOLOv8, its mAP50 and mAP50–95 increased by 0.017 and 0.005, respectively, while the parameter count and computational cost decreased by 0.37 M and 1.6 GFLOPs.

The rapid advancement of deep learning has enabled intelligent analysis in professional sports, yet tennis remains particularly challenging due to small and fast-moving objects, frequent occlusions, and complex backgrounds. To address these difficulties, we propose YOLO-Net, a lightweight detection framework tailored for tennis event analysis. Built upon YOLO11n, the framework integrates three task-oriented improvements: a C3k-MSEIS module for multi-scale edge enhancement and dual-domain feature selection to refine fine-grained boundaries; an ECA channel attention mechanism inserted after C2PSA to strengthen inter-channel dependency modeling and improve feature discriminability; and a Focaler-IoU loss function to emphasize hard and small samples while reducing localization errors. In addition, we construct and annotate a dedicated tennis dataset containing 6,648 images across three categories-player, racquet, and ball-covering diverse scenes, camera angles, and lighting conditions. Experimental results show that YOLO-Net achieves 84.5% precision and 78.2% mAP@0.5 with only 2.58M parameters, outperforming the YOLO11n baseline by 2.5% in precision and 0.9% in mAP while maintaining real-time inference. These findings demonstrate that YOLO-Net is an efficient, accurate, and deployable solution for applications such as referee assistance, tactical analysis, and intelligent broadcasting in tennis competitions.

Under the “dual carbon” goals, Taiyuan, a prefecture-level administrative unit and energy-intensive region in Shanxi Province, China, has experienced changes in soil carbon storage and soil carbon flux under rapid urbanization and industrialization. To clarify the spatial patterns of soil carbon storage and flux, 26 field sampling sites, including 78 soil samples, were analyzed using laboratory measurements and an optimized tunable diode laser absorption spectroscopy–geographic information system (TDLAS–GIS) integrated monitoring approach. This study investigated the spatial patterns of soil carbon storage and flux and discussed their potentially associated factors, providing an exploratory workflow for regional carbon monitoring. The results showed clear spatial heterogeneity, with an average soil organic carbon (SOC) content of 10.86 g/kg. High-SOC areas were mainly located in the southern and southwestern plains, while lower SOC levels occurred in urban expansion zones and highly disturbed surfaces. The western mountainous areas were important ecological barriers but were not the highest measured SOC zones. At the site level, arable land and forestland showed higher mean SOC values than grassland, with average SOC contents of 12.47, 12.07, and 8.27 g/kg, respectively, although these land-use-related differences were not statistically significant. Soil carbon flux was relatively higher in some mountainous regions and industrial–ecological transition areas but lower in several urban expansion areas. The results suggest that urbanization and industrial activity may be associated with changes in SOC and soil-atmosphere CO₂ exchange. This study describes the spatial variation characteristics of soil carbon storage and flux, establishes a reproducible TDLAS–GIS workflow for regional carbon monitoring, and provides exploratory support for ecological sustainability, sustainable land management, and the “dual carbon” strategy in northern resource-based cities.

Introduction: Existing detection models for automatic tea-grade determination in open-field, complex habitats suffer from insufficient feature robustness, weak suppression of background interference, and difficulty in balancing lightweight design with accuracy. To address these limitations, this study proposes Tea-DETR, a model tailored for open-air tea garden scenarios based on the end-to-end RT-DETR detection paradigm. Methods: The proposed method enhances feature representation and attention allocation from two aspects: backbone optimization and feature interaction enhancement. First, a lightweight backbone module is designed to strengthen long-range dependency modeling and improve suppression of complex background interference, thereby enhancing the robustness and discriminability of multi-scale tea-leaf features under occlusion and illumination variations. Second, an efficient attention-enhancement mechanism is introduced to reduce redundant feature interactions and enable adaptive focus on critical target regions, improving the model’s ability to capture subtle semantic differences among tea grades while maintaining computational efficiency. To validate the approach, Anji white tea from Zhejiang Province was selected as the experimental subject, and a single-leaf tea dataset containing 6,542 images was constructed. The dataset was split into training, validation, and test sets at a ratio of 8:1:1, based on which comparative experiments and convergence analyses were conducted. Results: With the number of parameters reduced to 14.65M, Tea-DETR achieves an accuracy of 92.2% and improves mAP@0.5 to 82.0%, reducing parameters by 26.8% compared with the baseline model. In addition, Tea-DETR exhibits markedly improved convergence stability and substantially enhanced capability in distinguishing subtle semantic differences among tea grades, effectively alleviating ambiguous discrimination under complex backgrounds. Discussion: Overall, the proposed method enhances the stability of fine-grained feature capture for small tea-leaf targets in open-field environments while maintaining real-time inference efficiency, providing a robust solution for real-time, non-destructive automatic tea-grade determination in field scenarios.

Wet direct-seeded rice (WDSR) is a resource-efficient cultivation system gaining global popularity, but its sustainability is severely threatened by weedy rice (Oryza sativa f. spontanea). Due to the high genetic and physiological similarities between weedy and cultivated rice, selective chemical control remains a formidable challenge. This study evaluated an integrated chemical control strategy utilizing the safener metcamifen (applied as a seed coating) to protect cultivated rice from the pre-emergence herbicide pretilachlor in a simulated WDSR system. Indoor bioassays and outdoor mock-plot trials revealed that metcamifen seed coating alone (up to 560 mg a.i. kg⁻¹ seed) significantly promoted early seedling vigor in cultivated rice (‘Jia 67’) without exhibiting phytotoxicity. Conversely, soil application of pretilachlor at 375 g a.i. ha⁻¹ provided effective initial herbicidal activity, suppressing weedy rice emergence to merely 7.0%. Under this severe herbicide stress, metcamifen seed coating at an effective dose of 480 mg a.i. kg⁻¹ seed significantly mitigated phytotoxicity. However, this protection was partial; crop emergence was maintained at 63.8%, substantially preserving seedling biomass compared to the non-safened control (28.3%), but still reflecting a clear emergence penalty. We hypothesize that this moderate reduction in initial crop stand could potentially be compensated by proportionally increasing the initial seeding rate—a potential agronomic compromise that warrants future empirical validation in the field. In summary, this study provides a preliminary, controlled-environment evaluation demonstrating that the protective application of metcamifen with pretilachlor offers a potential framework for mitigating weedy rice infestations, subject to further field-scale verification.

Aiming at the rain protection problem of outdoor precision instruments such as total stations, this paper designs an environmentally adaptive intelligent protection system based on D-S evidence fusion and state machine control. In terms of mechanical structure, the protective cover is designed as a sinking type, which not only improves the safety of the equipment but also avoids the shielding problem of the working surface compared with the traditional upward-lifting structure. The system collects data from multi-source meteorological sensors and uses D-S evidence theory for fusion decision-making. To alleviate decision conflicts in high-conflict scenarios, a conflict-guided dynamic weight adjustment strategy is introduced. Combined with a dual-layer finite state machine, the system realizes coordinated control between environmental perception and protection actions and can activate protection within 30 s under severe weather. Simulation results show that the improved method increases the response speed by 41.9–62.5% compared with traditional D-S fusion in weather transition conditions. In a 28-day field test, the system achieves a daily protection success rate of 96.4% and 100% reliability in critical weather transitions. The proposed system can provide reliable support for the all-weather safe operation of field precision measurement equipment.

Introduction: Timely identification and precise segmentation of tea leaf diseases are essential for intelligent agricultural management. However, balancing lightweight deployment and high-precision segmentation remains challenging under uneven illumination, background interference, and subtle early-stage lesion textures in natural environments. Methods: We propose TDS-YOLO, a lightweight segmentation model based on the YOLOv11 framework. The model introduces three innovations: (1) C3K2_EViM_CGLU for global dependency modeling, (2) EfficientHead for lightweight pixel-level representation, and (3) C2PSA_Mona to enhance multi-scale texture perception. Results: Experiments on a diverse dataset of 4,933 images show that TDS-YOLO achieves state-of-the-art performance with only 2.53M parameters. It reaches an mAP@0.5 of 90.1% for both detection and segmentation, outperforming YOLOv11-seg and other mainstream models while maintaining an inference speed of 96 FPS. Discussion: The proposed approach provides an efficient and robust solution for real-time monitoring of tea diseases, supporting precision tea plantation management and broader smart digital agriculture applications.

Rapid and uniform seedling establishment is critical for the productivity of direct-seeded rice, particularly in late-season cropping systems where sowing frequently coincides with high-temperature stress. Current seed quality assessment relies predominantly on the Standard Germination Test (SGT); however, this method, conducted under optimal conditions, often fails to predict field performance in thermally stressful environments. To resolve this discrepancy, this study established a High-Temperature Germination (HTG) protocol optimized specifically for late-season rice. Twenty-three diverse rice genotypes—comprising conventional japonica, indica-japonica hybrids, and indica hybrids—were evaluated using SGT and HTG assays at 35 °C, 38 °C, and 41 °C, incorporating a pre-treatment with trichloroisocyanuric acid (TCCA) to standardize initial seed conditions. Validation was conducted through field trials at two distinct locations in Zhejiang, China. The results demonstrated that while SGT indicated high viability (>85%) for most varieties, it exhibited a poor correlation with field emergence (r < 0.31). In contrast, HTG tests conducted at 38 °C and 41 °C showed reliable predictive validity, yielding highly significant correlations with field establishment (r > 0.70, p < 0.001). Significant genotypic variation was observed: hybrid varieties displayed superior thermotolerance and stable germination even at 41 °C, whereas conventional japonica varieties exhibited marked sensitivity to temperatures exceeding 35 °C. These findings highlight the potential of the HTG assay (specifically at 38 °C or 41 °C) as an effective, cost-efficient, and rapid screening tool. By accurately simulating the acute thermal stress of the sowing-to-emergence window, this method facilitates the identification of climate-resilient germplasm and supports reliable stand establishment in direct-seeded rice production.

Rice disease detection is vital for food security, prevention efficiency, pesticide reduction, and sustainable agriculture. Challenges like poor model appl