Automated Detection of Kinky Back in Broiler Chickens Using Optimized Deep Learning Techniques

The global poultry industry faces growing challenges from skeletal disorders, with Kinky Back (KB) significantly impacting broiler welfare and production. KB causes spinal deformities that reduce mobility, feed access, and increase mortality. It often remains undetected in early subclinical stages. Traditional KB diagnosis methods are slow and subjective, and highlighting the need for an automated and objective detection. This study develops a machine learning approach for detecting KB in broilers using image data. Male Cobb 500 broilers were raised under controlled conditions and monitored over 7 weeks using overhead 4K video cameras. Behavioral and posture data related to KB were collected and annotated from images extracted from the videos. First, various optimizers (SGD, Adam, AdamW), image sizes, and data augmentation techniques were compared, and the best-performing optimizer, image size, and data augmentation technique were identified. These findings were then used to compare different original lightweight YOLO models trained and to identify the best models with further modifications to these configurations, aiming to improve detection accuracy. Different machine vision models were evaluated using precision, recall, F1-score, and mean average precision metrics to identify the best-performing approach. Among the tested optimizers, SGD achieved the highest precision (100%) and mAP_0.50–0.95 (74.7%), indicating superior localization and lower false-positive rates, while AdamW produced the highest recall (98.9%) with slightly lower precision. Image input size of 960 × 960 pixels yielded the best balance of precision (99.0%), recall (99.4%), and F1-score (99.2%). Data augmentation improved recall and reduced false negatives by confirming its value in enhancing model generalization. Among YOLO architectures, YOLOv9 performs best. Furthermore, the optimized YOLOv9 model, combined with augmentation and 960-sized images, achieved the highest performance, with a precision of 99.1%, recall of 100%, F1-score of 99.5%, and mAP of 78.0%. Overall, the proposed optimized YOLOv9-based system provides a reliable and scalable framework for automated detection of Kinky Back, supporting data-driven welfare management in modern poultry production.