Abstract
Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual-stream architecture with weight-shared encoders, modality-specific self-attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual-gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel-level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs--outperforming state-of-the-art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual-task learning is essential for optimal performance. Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at this https URL.
Abstract (translated)
反刍酸中毒是一种在乳牛中常见的代谢紊乱，它会导致显著的经济损失和动物福利问题。当前的诊断方法依赖于侵入性的pH值测量，这限制了其用于连续监测的大规模应用的可能性。我们提出了FUME（融合统一多气体排放网络），这是首个针对体外条件下从双气体光学成像数据中检测反刍酸中毒的深度学习方法。我们的方法利用红外摄像机捕捉到的二氧化碳(CO2)和甲烷(CH4)排放模式来将瘤胃健康状态分类为健康、过渡期和酸性三个等级。 FUME采用了一种轻量级的双流架构，其中包括共享权重编码器、模态特定自注意力机制以及通道注意融合，这种方法能够同时优化气体羽状物分割和奶牛健康的分类。我们引入了首个包含8,967帧注释数据集的双气体OGI（光学气体成像）数据集，这些图像跨越六个不同的pH水平，并带有像素级别的分割掩模。 实验表明，FUME实现了80.99%的mIoU和98.82%的分类精度，而仅使用了1.28M参数和1.97G MACs（每秒百万乘法累加操作），在计算成本降低十倍的情况下超过了最先进的方法，在分割质量方面表现更佳。消融研究表明，CO2提供了主要的判别信号，并且双任务学习对于最佳性能至关重要。 我们的工作验证了基于气体排放监测家畜健康的可行性，为体外酸中毒检测系统的实际应用铺平了道路。代码可在该链接提供：[这个URL]（请根据实际情况填写正确的URL）。
URL
https://arxiv.org/abs/2601.08205
