In commercial poultry farming, automated behavioral monitoring systems hold significant potential for optimizing production efficiency and improving welfare outcomes at scale. The behavioral detection of free-range broilers matters for precision farming and animal welfare. Current research often focuses on either behavior detection or individual tracking, with few studies exploring their connection. To continuously track broiler behaviors, the Only Detect Broilers Once (ODBO) method is proposed by linking behaviors with identity information. This method has a behavior detector, an individual Tracker, and a Connector. First, by integrating SimAM, WIOU, and DIOU-NMS into YOLOv8m, the high-performance YOLOv8-BeCS detector is created. It boosts P by 6.3% and AP by 3.4% compared to the original detector. Second, the designed Connector, based on the tracking-by-detection structure, transforms the tracking task, combining broiler tracking and behavior recognition. Tests on sort-series trackers show HOTA, MOTA, and IDF1 increase by 27.66%, 28%, and 27.96%, respectively, after adding the Connector. Fine-tuning experiments verify the model’s generalization. The results show this method outperforms others in accuracy, generalization, and convergence speed, providing an effective method for monitoring individual broiler behaviors. In addition, the system’s ability to simultaneously monitor individual bird welfare indicators and group dynamics could enable data-driven decisions in commercial poultry farming management.

With the digital transformation of smart grids, power material management uses massive heterogeneous data, including structured procurement tables and unstructured demand texts. Existing multimodal models like TAMO fuse tabular and textual modalities to solve data silos, yet their static hard fusion brings extra noise. This paper proposes AdaTAMO, a prior-guided adaptive framework based on TAMO, with three core contributions. At the model level, it designs a domain knowledge-driven adaptive gating mechanism, using heuristic semantic rules to dynamically fuse text and table modalities on demand, reducing noise. At the data level, it builds Power-TableQA, a dedicated dataset for power material reasoning; the full dataset is private for compliance, but its construction pipeline and prompt templates are open-sourced for reproducibility. At the application level, it presents power grid material management scenarios and clarifies the model’s integration path. Experiments show that AdaTAMO performs comparably or better on general datasets, and outperforms baselines on the domain dataset, with higher query accuracy and interpretability for material demand decision making.

This study addresses the challenges of passion fruit visual maturity-class detection in complex natural orchard environments, particularly the missed detections of visually challenging fruit instances caused by background interference, occlusion, and illumination variations. To address this issue, an improved detection model, WEC-YOLO11, is proposed based on YOLO11n. The model incorporates the Efficient Channel Attention (ECA) mechanism to enhance discriminative feature representation while suppressing background interference, introduces the Content-Aware ReAssembly of Features (CARAFE) operator for content-aware upsampling, and adopts the WIoUv3 loss function with a dynamic non-monotonic focusing mechanism for bounding box regression. Across three independent runs, WEC-YOLO11 achieved an average mean average precision (mAP)@0.5 of 86.2% and an average recall of 81.1%, with 2.69 M parameters and 6.5 giga floating-point operations per second (GFLOPs). Compared to YOLO11n, the recall of ripe passion fruit (rpf) increased from 76.5% to 79.9% (+3.4 percentage points), while rpf precision showed a slight decrease from 77.6% to 77.1%, indicating a recall-oriented precision–recall trade-off. The overall recall increased from 79.9% to 81.1% (+1.2 percentage points). Under the tested batch-1 RTX 4090 setting, the measured throughput was 91.6 frames per second (FPS). These results indicate a favorable accuracy–complexity trade-off for passion fruit visual maturity-class detection within the evaluated public orchard dataset.