FUME: Fused Unified Multi-Gas Emission Network for Livestock Rumen Acidosis Detection

TL;DR

FUME is a deep learning system that detects rumen acidosis in dairy cattle by analyzing dual-gas optical images, achieving high accuracy with low computational cost, and demonstrating the potential for non-invasive livestock health monitoring.

Contribution

This work introduces the first dual-gas optical imaging dataset and a novel lightweight deep learning architecture for non-invasive rumen acidosis detection in cattle.

Findings

FUME achieves 80.99% mIoU in segmentation.

FUME attains 98.82% classification accuracy.

Dual-gas analysis outperforms single-gas methods.

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 https://github.com/taminulislam/fume.