Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro

Abstract This study investigated the possibility of using a laser methane detector (LMD) and optical gas imaging (OGI) to detect and quantify enteric methane () produced by ruminants in vitro. Four single‐flow continuous fermenters were used for rumen culture incubation with four different treatment diets: Control (50:50 forage to concentrate [F:C] ratio), Control + Bromoform (CBR), Low Forage (LF; 20:80), and High Forage (HF; 80:20). After 10 days of incubation, all fermenter contents were transferred and used in a 24 h ANKOM batch culture to measure gas production with LMD and OGI. The authors introduce the Controlled Diet (CD) dataset, a large‐scale collection of 4,885 plume images captured using an FLIR GF77 OGI camera under varying dietary conditions. The performance of six semantic segmentation models (FCN, U‐Net, Vision Transformer, Swin Transformer, DeepLabv3+, and Gasformer) on the CD dataset is compared. Results showed that LMD data for followed a similar pattern to the gas chromatography (GC) instrument results. The in vitro results showed that different diets and F:C ratios had an impact on gas production and rumen fermentation characteristics. Adding bromoform to the control diet fully inhibited emission. The HF diet produced more compared to all treatments () when measured with GC and LMD. CBR produced the lowest values when measured with GC and LMD. The Gasformer architecture achieved the highest performance with mean IoU of 85.1% and mean F‐score of 91.72%. These findings demonstrate that OGI technology combined with advanced semantic segmentation models offers a promising solution for predicting and quantifying emissions in the livestock sector, potentially aiding in the development of mitigation strategies to combat climate change.