YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases
Digestive diseases are common in poultry and significantly affect their health and productivity. Image processing techniques have gained attention for detecting early signs of disease by analyzing abnormal poultry manure. However, building a reliable system for identifying and locating abnormal manu...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375525003776 |
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| author | Wenxiang Qin Xiao Yang Yang Wang Yongxiang Wei Yan Zhou Weichao Zheng |
| author_facet | Wenxiang Qin Xiao Yang Yang Wang Yongxiang Wei Yan Zhou Weichao Zheng |
| author_sort | Wenxiang Qin |
| collection | DOAJ |
| description | Digestive diseases are common in poultry and significantly affect their health and productivity. Image processing techniques have gained attention for detecting early signs of disease by analyzing abnormal poultry manure. However, building a reliable system for identifying and locating abnormal manure in real-world conditions remains challenging and requires large amounts of labeled data for supervised learning. Among deep learning models, the You Only Look Once (YOLO) detector is widely used in precision agriculture due to its speed and accuracy. Herein, a new dataset was created for abnormal poultry manure identification based on updated classification criteria, which included 5688 bounding box annotations across five categories collected from commercial chicken farms. In total, 21 state-of-the-art YOLO models from 8 YOLO versions (YOLOv3–YOLOv9) were fine-tuned and validated to establish comprehensive benchmarks. Detection accuracy in terms of mAP@0.5 ranged from 95.6 % by YOLOv3-tiny to 99.4 % by YOLOv8m, while accuracy in terms of mAP@[0.5:0.95] ranged from 72.9 % by YOLOv4-tiny to 82.2 % by YOLOv9s, with 10 models achieving scores above 80.0 % in mAP@0.5:0.95. High accuracy and efficiency were demonstrated by YOLOv8n and YOLOv8s with inference times under 3 ms. However, traditional data augmentation methods were not fully effective in expanding abnormal manure samples. To address this issue, generative models were explored for data augmentation, where denoising diffusion probabilistic models generated realistic images, showing promising potential. This research provides benchmark data for the classification of abnormal poultry manure, which will become an important resource for promoting future research on poultry disease detection and control based on big data and artificial intelligence, making a fundamental contribution to the field of poultry health monitoring. |
| format | Article |
| id | doaj-art-2e7e25921d574876b6e52e2f4c8622ae |
| institution | Kabale University |
| issn | 2772-3755 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-2e7e25921d574876b6e52e2f4c8622ae2025-08-20T03:50:32ZengElsevierSmart Agricultural Technology2772-37552025-12-011210114510.1016/j.atech.2025.101145YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseasesWenxiang Qin0Xiao Yang1Yang Wang2Yongxiang Wei3Yan Zhou4Weichao Zheng5Department of Agricultural Structure and Environmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, 100083, Beijing, ChinaDepartment of Agricultural Structure and Environmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, 100083, Beijing, China; Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, 100083, Beijing, China; Beijing Engineering Research Center on Animal Healthy Environment, 100083, Beijing, ChinaDepartment of Agricultural Structure and Environmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, 100083, Beijing, China; Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, 100083, Beijing, China; Beijing Engineering Research Center on Animal Healthy Environment, 100083, Beijing, ChinaInstitute of Animal Husbandry, Henan Academy of Agricultural Science, 450002, Henan, ChinaZhucheng Zhongyu Electrical and Mechanical Equipment Co., Ltd., 262200, Zhucheng, ChinaDepartment of Agricultural Structure and Environmental Engineering, College of Water Resources and Civil Engineering, China Agricultural University, 100083, Beijing, China; Key Laboratory of Agricultural Engineering in Structure and Environment, Ministry of Agriculture and Rural Affairs, 100083, Beijing, China; Beijing Engineering Research Center on Animal Healthy Environment, 100083, Beijing, China; Corresponding author.Digestive diseases are common in poultry and significantly affect their health and productivity. Image processing techniques have gained attention for detecting early signs of disease by analyzing abnormal poultry manure. However, building a reliable system for identifying and locating abnormal manure in real-world conditions remains challenging and requires large amounts of labeled data for supervised learning. Among deep learning models, the You Only Look Once (YOLO) detector is widely used in precision agriculture due to its speed and accuracy. Herein, a new dataset was created for abnormal poultry manure identification based on updated classification criteria, which included 5688 bounding box annotations across five categories collected from commercial chicken farms. In total, 21 state-of-the-art YOLO models from 8 YOLO versions (YOLOv3–YOLOv9) were fine-tuned and validated to establish comprehensive benchmarks. Detection accuracy in terms of mAP@0.5 ranged from 95.6 % by YOLOv3-tiny to 99.4 % by YOLOv8m, while accuracy in terms of mAP@[0.5:0.95] ranged from 72.9 % by YOLOv4-tiny to 82.2 % by YOLOv9s, with 10 models achieving scores above 80.0 % in mAP@0.5:0.95. High accuracy and efficiency were demonstrated by YOLOv8n and YOLOv8s with inference times under 3 ms. However, traditional data augmentation methods were not fully effective in expanding abnormal manure samples. To address this issue, generative models were explored for data augmentation, where denoising diffusion probabilistic models generated realistic images, showing promising potential. This research provides benchmark data for the classification of abnormal poultry manure, which will become an important resource for promoting future research on poultry disease detection and control based on big data and artificial intelligence, making a fundamental contribution to the field of poultry health monitoring.http://www.sciencedirect.com/science/article/pii/S2772375525003776Poultry manure detectionDisease warningDeep learningGenerative AIData augmentation |
| spellingShingle | Wenxiang Qin Xiao Yang Yang Wang Yongxiang Wei Yan Zhou Weichao Zheng YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases Smart Agricultural Technology Poultry manure detection Disease warning Deep learning Generative AI Data augmentation |
| title | YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases |
| title_full | YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases |
| title_fullStr | YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases |
| title_full_unstemmed | YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases |
| title_short | YOLOPoul: Performance evaluation of a novel YOLO object detectors benchmark for multi-class manure identification to warn about poultry digestive diseases |
| title_sort | yolopoul performance evaluation of a novel yolo object detectors benchmark for multi class manure identification to warn about poultry digestive diseases |
| topic | Poultry manure detection Disease warning Deep learning Generative AI Data augmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2772375525003776 |
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