Lumpy Skin Disease (LSD) and Foot-and-Mouth Disease (FMD) are highly contagious viral diseases affecting cattle, causing significant economic losses and welfare challenges. Their visual diagnosis is complicated by significant symptom overlap with each other and with benign conditions like insect bites or chemical burns, hindering timely control measures. Leveraging a comprehensive dataset of 10,516 expert-annotated images from 18 farms across India, Brazil, and the USA, this study presents a novel Ensemble Deep Learning framework integrating VGG16, ResNet50, and InceptionV3 with optimized weighted averaging for simultaneous LSD and FMD detection. The model achieves a state-of-the-art accuracy of 98.2\%, with macro-averaged precision of 98.2\%, recall of 98.1\%, F1-score of 98.1\%, and an AUC-ROC of 99.5\%. This approach uniquely addresses the critical challenge of symptom overlap in multi-disease detection, enabling early, precise, and automated diagnosis. This tool has the potential to enhance disease management, support global agricultural sustainability, and is designed for future deployment in resource-limited settings.

翻译：结节性皮肤病（LSD）与口蹄疫（FMD）是影响牛群的高度传染性病毒性疾病，可造成重大经济损失并引发动物福利挑战。由于两者症状高度重叠，且与昆虫叮咬或化学灼伤等良性病症相似，其视觉诊断极为复杂，阻碍了及时防控措施的施行。本研究利用来自印度、巴西和美国18个农场的10,516张专家标注图像构建的综合数据集，提出了一种新颖的集成深度学习框架。该框架整合了VGG16、ResNet50与InceptionV3模型，并采用优化加权平均策略，实现了对LSD与FMD的同时检测。模型取得了98.2%的先进准确率，宏平均精确率、召回率与F1分数均为98.1%，AUC-ROC达到99.5%。该方法独特地解决了多疾病检测中症状重叠的关键挑战，能够实现早期、精准且自动化的诊断。该工具具备提升疾病管理水平、支持全球农业可持续性的潜力，并设计为未来可在资源有限环境中部署。