A Robust Support Vector Machine Model for Monitoring Methane Emission Levels in Paddy Ecosystems using Electronic Nose Technology

Authors

  • Mohd Muzamir Othman Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Muhamad Khairul Ali Hassan Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Sukhairi Sudin Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Fathinul Syahir Ahmad Saad Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Khairul Salleh Basaruddin Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Muhammad Juhairi Aziz Safar Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Shafriza Nisha Basah Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Haniza Yazid Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia
  • Mohd Hanafi Mat Som Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

Keywords:

Carbon Emission, Paddy Farming, Electronic Noise, Greenhouse Gas Emissions

Abstract

Paddy farming is a significant contributor to global greenhouse gas emissions, particularly methane (CH4) and carbon dioxide (CO2). While accurate monitoring is vital for carbon management, traditional analytical methods are often cost-prohibitive and technically complex. This study develops a robust Support Vector Machine (SVM) model integrated with Electronic Nose (E-Nose) technology to predict carbon emission levels by analyzing multi-dimensional environmental and gas parameters. A comprehensive dataset was collected across three field locations (water inlet, mid-field, and water outlet), comprising CHand COconcentrations, ambient temperature, humidity, and rice growth stages. The data underwent a rigorous pre-processing pipeline, including median imputation for missing values and outlier removal using the Interquartile Range (IQR) method, which refined the dataset from 1,499 to 1,486 instances. Feature selection via the SelectKBest method identified CHand COconcentrations, growth stages, and gas fluxes as the most significant predictors. The SVM model utilizing a Radial Basis Function (RBF) kernel was evaluated using two configurations: "All Features" and "Selected Features". The "Selected Features" model demonstrated superior predictive performance on the testing set, achieving a coefficient of determination Rof 0.9975, a Mean Absolute Error (MAE) of 0.0067, and a Root Mean Square Error (RMSE) of 0.0085. In comparison, the "All Features" model yielded a slightly lower Rof 0.9918, with a higher MAE of 0.0122 and RMSE of 0.0153. This indicates that isolating key environmental drivers not only reduces computational redundancy but also enhances the model's accuracy and generalizability. The results demonstrate that an optimized SVM model, supported by strategic feature selection, provides a highly reliable and cost-effective tool for monitoring carbon footprints in paddy ecosystems. This framework facilitates the transition toward data-driven sustainable farming and precision emission management.

Author Biographies

Mohd Muzamir Othman, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

syahmir90@gmail.com

Muhamad Khairul Ali Hassan, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

khairulhassan@unimap.edu.my

Sukhairi Sudin, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

sukhairi@unimap.edu.my

Fathinul Syahir Ahmad Saad, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

fathinul@unimap.edu.my

Khairul Salleh Basaruddin, Faculty of Mechanical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

ksalleh@unimap.edu.my

Muhammad Juhairi Aziz Safar, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

juhairi@unimap.edu.my

Shafriza Nisha Basah, Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

shafriza@unimap.edu.my

Haniza Yazid, Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

hanizayazid@unimap.edu.my

Mohd Hanafi Mat Som, Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Kampus Pauh Putra, Arau, Perlis, 02600, Malaysia

mhanafi@unimap.edu.my

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Published

2026-06-03

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Articles