An IoT-Enabled Framework for Real-Time Monitoring and Prediction of Methane Emissions in Sustainable Ruminant Farming
Article Sidebar
Main Article Content
Abstract
This paper presents the development of an Artificial Intelligence (AI) enabled Internet of Things (IoT) framework for methane emission monitoring and prediction in sustainable ruminant farming. Methane emissions from ruminant livestock are a significant contributor to greenhouse gas emissions and a major environmental concern in sustainable agriculture. Conventional methods for measuring and controlling these emissions are manual, time-consuming, and lack predictive intelligence, thereby limiting farmers’ ability to make timely, data-driven decisions. This paper addresses these challenges by integrating IoT-based sensing and AI-driven predictive analytics to enable real-time data acquisition, intelligent forecasting, and emission control for livestock management. The IoT subsystem, designed and simulated in Proteus, comprises a methane gas sensor, an ATmega328P microcontroller, an ESP8266 Wi-Fi module, and a cloud-based Blynk dashboard. Simulation results confirmed stable data transmission, accurate methane detection across varying concentrations, and real-time visualization on a mobile interface. The AI component utilized a comprehensive dataset of feed composition, animal weight, and environmental variables collected from ruminant farms across South–South Nigeria. Three supervised learning algorithms, Random Forest, XGBoost, and Artificial Neural Network (ANN), were retrained and evaluated using performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The Random Forest model outperformed the others with MAE = 1.52, RMSE = 2.21, and predictive accuracy of 93%. The integrated AI–IoT system demonstrates the ability to monitor methane emissions continuously, predict future trends, and generate actionable insights to optimise feed strategies and livestock performance. This hybrid approach contributes to greenhouse gas mitigation, precision livestock management, and environmental sustainability in modern agriculture.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
L. U. Silvia and G. R. Sinha, “Advancements in smart environmental monitoring systems using IoT and sensor technologies,” Journal of Environmental Monitoring and Assessment, vol. 192, no. 8, pp. 548–560, 2020. DOI: https://doi.org/10.3390/s20113113
M. F. Dida , M. B. Shirvan , T. Kawaguchi , S. C. Garcia . “Potential applications of a low-cost gas sensor to monitor enteric methane emission from ruminant animals,” Smart Agricultural Technology, Vol 10. 2025. DOI: https://doi.org/10.1016/j.atech.2024.100706
G. K. Sinha. “Sensor data analytics for optimized methane leak detection and mitigation,” International Journal of Science and Research (IJSR), Vol.13, no.4, pp. 223–231, 2024.DOI: https://doi.org/10.21275/SR24330010938
R. Majumder, J. Pollard, M. S. Salek, D. Werth, G. Comert, A. Gale, S. M. Khan, S. Darko, and M. Chowdhury, “Development and evaluation of ensemble learning-based environmental methane detection and intensity prediction models,” International Journal of Distributed Sensor Networks, vol. 20, no. 1, pp. 1–18, 2024, DOI: https://doi:10.1177/11786302241227307.
R. Luo, J. Wang, and I. Gates, “Forecasting methane data using multivariate long short-term memory neural networks,” Environmental Modelling & Assessment, vol. 29, pp. 441–454, 2024,DOI: https://doi:10.1007/s10666-024-09957-x.
Z. Wang, S. Xiao, J. Wang, A. Parab, and S. Patel, “Reinforcement learning-based agricultural fertilisation and irrigation considering N₂O emissions and uncertain climate variability,” AgriEngineering, vol. 7, no. 8, p. 252, 2025, DOI: https://doi:10.3390/agriengineering7080252.
N. K. Almazmomi, “Artificial intelligence-driven blockchain and Internet of Things framework for secure data management in precision agriculture,” Journal of Information Technology & Control, vol. 31, no. 3, 2025, DOI: https://doi:10.1177/09266801251331816.
H. Akcay and C. Durmaz, “Monitoring methane emissions in livestock with an IoT-based early warning system,” Mediterranean Agricultural Sciences, vol. 38, no. 3, pp. 159–162, 2025, DOI: https://doi.org/10.29136/mediterranean.1786067
J. Ghassemi Nejad et al., “Advances in methane emission estimation in livestock: A review of data collection methods, model development and the role of AI technologies,” Animals, vol. 14, no. 3, p. 435, 2024, DOI: http://doi.org/10.3390/ani14030435
D. C. Popa, Y. Laurent, R. A. Popa, et al., “A platform for GHG emissions management in mixed farms,” Agriculture, vol. 14, no. 1, p. 78, 2023, DOI: http://doi.org/10.3390/agriculture14010078
L. O. Tedeschi et al., “Advancing precision livestock farming: integrating artificial intelligence and emerging technologies for sustainable livestock management,” Animal Bioscience, vol. 39, no. 4, 2025, DOI: http://doi.org/10.5713/ab.25.0289
P. Aditya, M. Omeed, and P. Pramod, “Design and implementation of IoT-enabled device for real-time monitoring of greenhouse gas emissions and pressure in anaerobic reactors,” IEEE Access, vol. 12, no. 1, pp. 133848–133862, 2024.DOI: https://doi.org/10.1109/ACCESS.2024.3449217
Y. Chen, Y. Xie, X. Dang, B. Huang, and D. Jiao, “Spatiotemporal prediction of carbon emissions using a hybrid deep learning model considering temporal and spatial correlations,” Environmental Modelling & Software, vol. 172,DOI: https://doi.org/10.1016/j.envsoft.2023.105937