Yak daily behaviors, including feeding, standing, lying down, and walking, are closely related to their health status, making accurate behavior recognition essential for intelligent monitoring and management in yak husbandry. However, real-world grazing environments present significant challenges due to complex backgrounds, occlusions, small or distant targets, and high visual similarity between behavior categories. To address these issues, we propose a problem-driven, multi-scale behavior recognition framework based on an enhanced YOLOv11n architecture specifically designed for outdoor yak monitoring. A dedicated real-world dataset is constructed to capture four fundamental behaviors under diverse natural conditions. Based on this dataset, we develop the DPAP-YOLOv11n model, which incorporates Dynamic Convolution for adaptive feature modulation and Pinwheel-shaped Convolution (PConv) for fine-grained spatial representation. Additionally, a YOLOv7-Aux auxiliary training head is introduced to strengthen intermediate feature learning, and a Focal-PIoU loss function is adopted to improve robustness against hard or ambiguous samples. Experimental results show that DPAP-YOLOv11n outperforms the baseline YOLOv11n, achieving gains of 2.4% in mAP@50 and 2.8% in mAP@50–95. These findings demonstrate the practical potential of the proposed approach for high-precision, real-time yak behavior recognition in complex field environments.

Farmland changes have a profound impact on agricultural ecosystems and global food security, making the timely and accurate detection of these changes crucial. Remote sensing image change detection provides an effective tool for monitoring farmland dynamics, but existing methods often struggle with high-resolution images due to complex scenes and insufficient multi-scale information capture, particularly in terms of missed detections. Missed detections can lead to underestimating land changes, which affects key areas such as resource allocation, agricultural decision-making, and environmental management. Traditional CNN-based models are limited in extracting global contextual information. To address this, we propose a CNN-Transformer-based Multi-Scale Attention Siamese Network (MT-SiamNet), with a focus on reducing missed detections. The model first extracts multi-scale local features using a CNN, then aggregates global contextual information through a Transformer module, and incorporates an attention mechanism to increase focus on key change areas, thereby effectively reducing missed detections. Experimental results demonstrate that MT-SiamNet achieves superior performance across multiple change detection datasets. Specifically, our method achieves an F1 score of 65.48% on the HRSCD dataset and 75.02% on the CLCD dataset, significantly reducing missed detections and improving the reliability of farmland change detection, thereby providing strong support for agricultural decision-making and environmental management.

Rapid urbanization and continuous population growth have made waste management a critical global challenge. In this study, we construct the Waste4 dataset and propose a lightweight waste detection framework, FLEX-YOLO, to improve detection accuracy and computational efficiency in complex environments. Specifically, an Efficient Multi-Scale Feature Fusion Network (EMBSFPN) is developed to enhance multi-scale feature representation for waste objects with varying scales. The backbone is further enhanced by incorporating MogaBlock into the C3k2 component to improve local–global feature modeling. Moreover, a Dynamic Task-Aligned Detection Head (DTADH) is introduced to strengthen the interaction between classification and localization through shared feature learning and dynamic alignment strategies. An Efficient Multi-Scale Attention (EMA) mechanism is incorporated before the detection head to suppress background interference and improve target feature discrimination. Experimental results on the Waste4 dataset demonstrate that FLEX-YOLO achieves 97.0% mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.50 (mAP@50) and 74.0% mAP@50–95. Compared with YOLOv11 and the Real-Time Detection Transformer (RT-DETR), FLEX-YOLO improves mAP@50 by 3.5 and 4.0 percentage points, respectively, and mAP@50–95 by 3.8 and 3.0 percentage points, respectively. Additional experiments on the Trash Detection Dataset (TDD) show that FLEX-YOLO improves mAP@50 and mAP@50–95 by 2.2 and 1.9 percentage points, respectively, compared with YOLOv11. Furthermore, deployment on Jetson Orin Nano and Android platforms demonstrates its real-time inference capability and practical applicability. The proposed framework provides an efficient approach for real-world waste detection under resource-constrained edge deployment scenarios.