用YOLOv8自动识别鸡只病态，提升养鸡场健康监控效率
YOLOv8-Based Deep Learning Model for Automated Poultry Disease Detection and Health Monitoring paper
- 基于YOLOv8模型分析高分辨率鸡群图像，实时检测异常行为与外观
- 在大规模标注数据集上训练，实现感染鸡只的精准识别与及时预警
- 适合规模化养殖场用于早期疾病发现，降低人工巡检成本
在家禽产业中，及时发现鸡只疾病对避免经济损失至关重要。传统方法依赖人工观察，耗时且易出错。本文提出一种基于深度学习的智能系统，利用YOLOv8模型进行实时目标识别，通过分析高分辨率鸡群照片，检测包括行为和外观在内的疾病征兆。研究采用大规模标注数据集训练算法，可实现对感染鸡只的准确实时识别，并向养殖场管理者发出预警，支持快速响应。该AI技术有助于实现早期感染识别，减少人工检查需求，提升大型养殖场的生物安全水平。YOLOv8的实时特性为改善农场管理提供了可扩展、高效的技术方案。
原文摘要 · Abstract (English)
In the poultry industry, detecting chicken illnesses is essential to avoid financial losses. Conventional techniques depend on manual observation, which is laborious and prone to mistakes. Using YOLO v8 a deep learning model for real-time object recognition. This study suggests an AI based approach, by developing a system that analyzes high resolution chicken photos, YOLO v8 detects signs of illness, such as abnormalities in behavior and appearance. A sizable, annotated dataset has been used to train the algorithm, which provides accurate real-time identification of infected chicken and prompt warnings to farm operators for prompt action. By facilitating early infection identification, eliminating the need for human inspection, and enhancing biosecurity in large-scale farms, this AI technology improves chicken health management. The real-time features of YOLO v8 provide a scalable and effective method for improving farm management techniques.
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