Methane emissions from livestock are a major environmental concern, contributing approximately 14% of total anthropogenic greenhouse gas (GHG) emissions from agriculture. Among ruminants, eructation (belching) is a key physiological process through which most methane (CH₄) is released. In this study, we present a livestock-wearable system designed to detect belching events using inertial information, incorporating 6-degree-of-freedom inertial measurement units (IMU) placed around the animal’s head to capture mechanical vibrations linked to eructation. A low-power commercial micro-electromechanical methane gas sensor was integrated in order to simplify the annotation process during the data collection stages, enabling a rapid, scalable and human-independent labeling strategy. Machine learning (ML) models were evaluated and trained to anticipate eructation events based only on the IMU sensor data, while using the commercial CH₄ sensor to label significant events (emissions above a certain concentration threshold) in real-time. Beyond this labeling process, during the training and testing stages, the CH₄ sensor information is not required and all estimations rely only on the IMU inertial readings. Field validation demonstrated prediction accuracies of up to 79. 7% for individual subjects, providing results that suggest substantial potential for the accurate estimation of these belching events, under natural grazing conditions. These findings highlight the potential of integrating IMU-based sensing and ML algorithms as a scalable, minimal invasive alternative for methane monitoring in livestock. The approach can support better understanding of methane emission dynamics and inform mitigation strategies in precision livestock farming.
Moncayo et al. (Sat,) studied this question.