Effect of Soil Attribute on Greenhouse Gas Emission from Agriculture using IoT

A farmer uses different agriculture practices to improve the crop yield generating the greenhouse gases (GHGs). The soil management practices are very important in the greenhouse gas emission from agriculture sector. In this paper we tried to find the greenhouse gas CH
₄
and CO
₂
emission from agriculture using soil attributes i.e. soil moisture, soil temperature, humidity, pH values, and N-P-K values. We used different machine learning algorithm such as Decision table, SOM regression, ZeroR, Randomforest, Multilayer Perception and Multiple Linear regression for comparison purpose. We collected soil sample using different sensors from the farm. We have used Mean absolute error(MAE) and Root mean squared error (RMSE) as a coefficient of correlation with 10 fold cross validation. The result shows that for CH
₄
eq and CO
₂
eq the SOMregressor performs better than other classifiers with perfect positive correlation whereas the ZeroR classifier gives negative correlation for CO
₂
eq and CH
₄
eq emission. Also the OLS statistics of multiple linear regression result shows that R squared value 0.786 means 78% predicted values are similar to actual reading of CH
₄
eq emission.