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Research demonstrates disease detection in broiler chickens using feces images, highlighting the effectiveness of a modified yolo v5 algorithm.
This research focuses on detecting infectious diseases in broiler chickens through feces images using a modified YOLOv5 algorithm. Early disease detection is crucial for maintaining product quality and protecting consumer health. YOLOv5’s high detection speed and accuracy make it suitable for identifying multiple objects, enabling farmers to anticipate and mitigate disease risks. The study used a dataset of 8,067 images across four categories: coccidiosis, healthy, NCD, and salmonella, split into 90:10 and 80:20 training-validation ratios. Six modified YOLOv5 models were tested. Key modifications included adding bottleneck layers, replacing SiLU activation with ELU, and incorporating CBAM in various configurations. The best result, with a 90.0% mAP, came from the sixth modification (C3CBAM layer) with a 90:10 split. Testing via a mobile application showed the second (80:20 split) and fourth (90:10 split) models excelled in detecting both large and small objects.
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Utomo et al. (2025) studied this question.
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