article · Agriculture
Intensive broiler farming faces significant challenges in disease management and flock observation across variable lighting environments. To support automated poultry house monitoring, a newly compiled dataset of 10,000 visual and thermal images containing 50,000 annotations was created. These images label healthy broilers alongside specific pathological conditions, including lethargy, slipped tendons, eye disease, open-beak stress, and pendulous crop. Three versions of the YOLO algorithm, namely versions 5, 7, and 8, were trained on augmented sets of visual and thermal images to detect birds and classify these health issues under complex rearing conditions. The thermal YOLOv8 model yielded the strongest results, reaching an object detection mAP50 of 0.988 and an F1 classification score of 0.972. Combining visual and thermal monitoring provides poultry managers with complementary viewpoints, improving the overall reliability of automated health and welfare surveillance in intensive housing.
Rising demand for poultry requires intensive rearing systems where observing flock health and controlling disease are persistent difficulties. Automated systems that reliably identify sick or distressed birds under varying light conditions can reduce monitoring burdens. By detecting signs of illness such as lethargy or stress, automated image analysis offers an effective method to maintain poultry welfare and support intensive farm management.
This research presents an applied software model tested on visual and thermal image datasets. It could enable poultry farmers and automated farm management equipment providers to deploy non-invasive health screening tools within commercial broiler houses. While the algorithm shows strong experimental accuracy in classifying specific pathological conditions, the abstract does not indicate that the system has yet been deployed or evaluated in a commercial production environment, placing it at the applied testing stage.
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The increasing broiler demand due to overpopulation and meat imports presents challenges in poultry farming, including management, disease control, and chicken observation in varying light conditions. To address these issues, the development of AI-based management processes is crucial, especially considering the need for detecting pathological phenomena in intensive rearing. In this study, a dataset consisting of visual and thermal images was created to capture pathological phenomena in broilers. The dataset contains 10,000 images with 50,000 annotations labeled as lethargic chickens, slipped tendons, diseased eyes, stressed (beaks open), pendulous crop, and healthy broiler. Three versions of the YOLO-based algorithm (v8, v7, and v5) were assessed, utilizing augmented thermal and visual image datasets with various augmentation methods. The aim was to develop thermal- and visual-based models for detecting broilers in complex environments, and secondarily, to classify pathological phenomena under challenging lighting conditions. After training on acknowledged pathological phenomena, the thermal YOLOv8-based model demonstrated exceptional performance, achieving the highest accuracy in object detection (mAP50 of 0.988) and classification (F1 score of 0.972). This outstanding performance makes it a reliable tool for both broiler detection and pathological phenomena classification, attributed to the use of comprehensive datasets during training and development, enabling accurate and efficient detection even in complex environmental conditions. By employing both visual- and thermal-based models for monitoring, farmers can obtain results from both thermal and visual viewpoints, ultimately enhancing the overall reliability of the monitoring process.
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DOI: 10.3390/agriculture13081527
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