Falconry, an ancient practice of training and hunting with falcons, emphasizes the need for vigilant health monitoring to ensure the well-being of these highly valued birds, especially during hunting activities. This research paper introduces a cutting-edge approach, which leverages the power of Concatenated ConvNeXt and EfficientNet AI models for falcon disease classification. Focused on distinguishing 'Normal,' 'Liver,' and 'Aspergillosis' cases, the study employs a comprehensive dataset for model training and evaluation, utilizing metrics such as accuracy, precision, recall, and f1-score. Through rigorous experimentation and evaluation, we demonstrate the superior performance of the concatenated AI model compared to traditional methods and standalone architectures. This novel approach contributes to accurate falcon disease classification, laying the groundwork for further advancements in avian veterinary AI applications.

翻译：猎鹰训练是一项古老的驯鹰狩猎活动，其过程需对高价值猎鹰的健康状况进行密切监测，尤其在狩猎期间。本研究提出一种前沿方法，利用串联ConvNeXt与EfficientNet AI模型实现猎鹰疾病分类。研究聚焦于区分“正常”、“肝脏疾病”与“曲霉菌病”三类情况，采用综合数据集进行模型训练与评估，并运用准确率、精确率、召回率和F1分数等指标。通过严谨的实验与评估，我们证明串联AI模型相较于传统方法与独立架构具有更优性能。这一创新方法为猎鹰疾病精准分类提供了有效方案，为禽类兽医AI应用的进一步发展奠定了基础。