Search Results (3)

Infectious bursal disease (IBD) is a major immunosuppressive disease affecting young chickens, and causes significant economic losses to the poultry industry. This work represents the first pathogenicity assessment of Moroccan very virulent IBD virus. Molecular characterization and sequence analysis of this isolate previously identified specific substitutions, including seven amino acid substitutions in segment A, and I472L and E688D in segment B, specific and unique to Moroccan vvIBDV strains. Two chicken lines, broiler and specific-pathogen-free (SPF) chickens, were inoculated via the occulonasal route with 0.2 mL of the 10⁵EID₅₀ /mL viral solution of the IB19 vvIBDV strain at 29 days of age. Experimental monitoring was carried out for 10 days post-challenge (dpc). Clinical signs started on the second dpc, with peak severity observed between 3 and 6 dpc. The total mortality rate reached 10% in broilers (group G1) and 93% in SPF chickens (G3). Macroscopic lesions in G1 broilers included marked hypertrophy of the bursa of Fabricius (BF), followed by very pronounced atrophy, while macroscopic examinations of deceased SPF birds (G3) revealed very hemorrhagic BF with a black cherry appearance in 80% of dead birds. The mean Bursa/Body Index (BBI) of challenged broilers (G1) showed a decrease of 46% compared to the control group (G2), indicating bursal atrophy. Microscopic lesions in the BF consisted mainly of inflammation, with severe lymphoid depletion of the follicles in challenged G3 SPF birds. This in vivo study of Moroccan vvIBDV demonstrated a distinctive virulence profile, and confirmed its classification as a very virulent strain with substantial disease-causing potential. It is crucial to obtain comprehensive knowledge of the prevalence, emergence, pathogenicity, and control of Moroccan IBDV strains. Full article

The presence of dead broilers within a flock can be significant vectors for disease transmission and negatively impact the overall welfare of the remaining broilers. This study introduced a dead broiler detection method that leverages the fact that dead broilers remain stationary within the flock in videos. Dead broilers were identified through the analysis of the historical movement information of each broiler in the video. Firstly, the frame difference method was utilized to capture key frames in the video. An enhanced segmentation network, YOLOv8-SP, was then developed to obtain the mask coordinates of each broiler, and an optical flow estimation method was employed to generate optical flow maps and evaluate their movement. An average optical flow intensity (AOFI) index of broilers was defined and calculated to evaluate the motion level of each broiler in each key frame. With the AOFI threshold, broilers in the key frames were classified into candidate dead broilers and active live broilers. Ultimately, the identification of dead broilers was achieved by analyzing the frequency of each broiler being judged as a candidate death in all key frames within the video. We incorporated the parallelized patch-aware attention (PPA) module into the backbone network and improved the overlaps function with the custom power transform (PT) function. The box and mask segmentation mAP of the YOLOv8-SP model increased by 1.9% and 1.8%, respectively. The model’s target recognition performance for small targets and partially occluded targets was effectively improved. False and missed detections of dead broilers occurred in 4 of the 30 broiler testing videos, and the accuracy of the dead broiler identification algorithm proposed in this study was 86.7%. Full article

Improving productivity in industrial farming is crucial for precision agriculture, particularly in the broiler breeding sector, where swift identification of dead broilers is vital for preventing disease outbreaks and minimizing financial losses. Traditionally, the detection process relies on manual identification by farmers, which is both labor-intensive and inefficient. Recent advances in computer vision and deep learning have resulted in promising automatic dead broiler detection systems. In this study, we present an automatic detection and segmentation system for dead broilers that uses transformer-based dual-stream networks. The proposed dual-stream method comprises two streams that reflect the segmentation and detection networks. In our approach, the detection network supplies location-based features of dead broilers to the segmentation network, aiding in the prevention of live broiler mis-segmentation. This integration allows for more accurate identification and segmentation of dead broilers within the farm environment. Additionally, we utilized the self-attention mechanism of the transformer to uncover high-level relationships among the features, thereby enhancing the overall accuracy and robustness. Experiments indicated that the proposed approach achieved an average IoU of 88% on the test set, indicating its strong detection capabilities and precise segmentation of dead broilers. Full article