In large-scale caged broiler farms, daily inspection of dead broilers is essential for flock health management and disease prevention. To address the significant performance degradation of existing methods under challenging conditions such as poor lighting, severe occlusion, and complex backgrounds, this paper proposes a dual-modal dynamic hybrid convolutional feature fusion method for dead bird detection based on an improved YOLO11 framework, termed YOLO11-DualDynConv-FF. First, a dual-modal fusion network architecture was developed to combine RGB and infrared (IR) images, enabling the model to simultaneously process both modalities. By integrating complementary information from RGB and IR data, the proposed method significantly improved detection accuracy and efficiency under low-light conditions. Second, a dynamic hybrid convolution feature fusion module was designed to merge multi-scale feature maps with contextual information, allowing the network to capture fine-grained details and adapt better to complex farming environments. In addition, an occlusion-aware module was introduced to specifically address the physical occlusion challenges prevalent in crowded cage settings. Comparative experiments and ablation studies involving multiple object detection networks were conducted to evaluate the proposed method. The results show that the improved YOLO11 model achieves superior performance, with precision, recall, F1-score, and mAP@0.5 reaching 92.6%, 79.0%, 0.85, and 80.1%, respectively. These results represent improvements of 2.0%, 5.0%, 0.17, and 12.1%, respectively, over the original YOLO11 model. Compared with existing approaches, the proposed model is better suited to complex real-world poultry farming environments and achieves higher detection accuracy, providing a valuable reference for intelligent monitoring in caged poultry farming.

In caged broiler houses, dead-broiler detection typically relies on manual inspection, which is labor-intensive, inefficient, and difficult for high-level cages. To address these problems, this study proposed an automatic detection system for dead broilers in cage environments and deployed it on an inspection-robot platform for automated monitoring. First, to mitigate complex lighting and image blur, Gamma transform and Gaussian blur were applied for data augmentation to simulate real breeding scenes and improve model robustness. Second, the DDC-YOLO (DeadChicken-YOLO) detection model was developed; it uses a UIB lightweight backbone, the WIoU loss, the SimAM attention mechanism, and a DBFF feature-fusion module to enhance feature extraction and target detection while reducing model complexity, combined with TensorRT on edge devices to accelerate inference. Finally, a self-iterative mechanism was implemented that links robot data collection and detection with cloud-based transfer learning and model backhaul updates so the model could continuously adapt to new scenarios, such as different broiler breeds after deployment. The experimental results showed that the proposed DDC-YOLO achieved a precision of 96.8%, a recall of 85.4%, an F1 score of 90.8%, and mAP@0.5 of 93.1%. Compared with the original YOLO11, the model reduced parameters by 1.437 M and decreased size by 2.8 MB. In real farm environments, the model achieved real-time, accurate detection of dead broiler chickens with the continuous migration and adaptation across broiler-breed scenarios. Overall, the proposed method balanced detection accuracy, edge-deployment capability, and continuous optimization potential, and it can provide technical support for unmanned and reduced-staff broiler cage farming.

In commercial floor-rearing environments for yellow-feathered broilers, visual screening of broilers with observable health-related abnormalities is challenged by low illumination, individual occlusion, and the limited use of local comb information. This study proposes a two-stage global–local framework for yellow-feathered broilers. Images were collected from one commercial floor-rearing farm; after screening, 1463 images were used for key-region localization, and 2031 samples were constructed for health-status recognition. Samples were labeled as Healthy or Unhealthy by expert consensus according to observable phenotypic characteristics, rather than veterinary-confirmed disease diagnoses. First, a Dual-Target YOLO (DT-YOLO) model localized whole-body and comb regions. Second, a Transformer network integrated features from paired or single available inputs for classification. DT-YOLO achieved a mean Average Precision (mAP) of 97.56% at the 0.5 IoU threshold. For paired whole-body–comb inputs, the proposed classification model achieved an Accuracy of 97.11%, Macro-Precision of 97.38%, Macro-Recall of 96.58%, and Macro-F1 of 96.95%. Under the mixed-input condition, the model maintained an Accuracy of 96.06% and a Macro-F1 of 96.02%. These findings indicate that combining whole-body and comb information can support visual screening of unhealthy broilers under the single-farm floor-rearing conditions represented in the current dataset. Its applicability to other farms, breeds, and management conditions requires independent external validation.

In recent years, real-time monitoring of broiler chicken weight has become crucial for assessing growth and health status. Currently, obtaining weight data often relies on manual collection. However, this process is cumbersome, labor-intensive, and inefficient. This paper proposes a broiler carcass weight detection model based on deep learning image segmentation and regression to address these issues. The model first segments broiler carcasses and then uses the pixel area of the segmented region as a key feature for a regression model to predict weight. A custom dataset comprising 2709 images from 301 Taihu yellow chickens was established for this study. A novel segmentation network, AR2U-AtNet, derived from R2U-Net, is proposed. To mitigate the interference of background color and texture on target carcasses in slaughterhouse production lines, the Convolutional Block Attention Module (CBAM) is introduced to enable the network to focus on areas containing carcasses. Furthermore, broilers exhibit significant variations in size, morphology, and posture, which impose high demands on the model’s scale adaptability. Selective Kernel Attention (SKAttention) is therefore integrated to flexibly handle broiler images with diverse body conditions. The model achieved an average Intersection over Union (mIoU) score of 90.45%, and Dice and F1 scores of 95.18%. The regression-based weight prediction achieved an $R^2$ value of 0.9324. The results demonstrate that the proposed method can quickly and accurately determine individual broiler carcass weights, thereby alleviating the burden of traditional weighing methods and ultimately improving the production efficiency of yellow-feather broilers.

Cryopreservation plays a critical role in the long-term preservation of biological materials and has become an indispensable technology in biomedicine, agriculture, animal breeding, and biodiversity conservation. Despite substantial advances in cryobiology, the preservation of cells and tissues at ultra-low temperatures remains limited by cryoinjuries associated with ice crystal formation, osmotic stress, oxidative damage, and cellular dehydration. This review provides an overview of the historical development and fundamental principles of cryopreservation and critically summarizes the key factors affecting cryopreservation efficiency, including cryoprotective agents, antioxidants, osmotic balance, freezing and thawing protocols, and emerging ice-controlling materials. In addition, recent advances in the cryopreservation of diverse biological materials—such as sperm, oocytes, embryos, stem cells, and gonadal tissues from different animal species—are critically discussed, with particular attention to species-specific differences and the distinction between post-thaw survival and functional recovery. Finally, the current limitations of existing cryopreservation technologies and future research directions are highlighted to facilitate the development of safer and more effective preservation strategies for animal genetic resource conservation and biomedical applications.