Assessing CO2 Emission Trend and its Impact on Agricultural Crop Health using Machine Learning Technique

Atmospheric carbon emissions are one of the main contributors to climate change. This article explores trends in India’s CO 2 emission and its consequences on agricultural crop production. It also aims to explore the likely contributing factors responsible for influencing this trend through modeling. This understanding can not only help us to control the CO 2 emission problem but also can help in managing it in a meaningful way. We have implemented a Mann-Kendall (MK) test to identify the trend in CO 2 emission by utilizing the historical data. Building on the outcome of MK test, we developed a predictive model for analyzing CO 2 emission by incorporating socio-economic, industrial, and energy consumption data. A review of existing literature reveals the extensive use of machine learning techniques including support vector machines, decision trees, ensemble learning, and neural networks in this field. This article presents a hybrid technique known as Inclusive Multiple Model ANN-PSO (IMM-ANN-PSO) by leveraging the goodness of standard machine learning techniques. The outcome of the MK test suggests an increasing trend in CO 2 emission in the Indian context. While further investigation, modeling of CO 2 emission using the hybrid approach (IMM-ANN-PSO) showed significant improvement in the predictive performance with an impressive R-square value of 0.998 and a lowest RMSE value of 0.054. This is better compared to the individual performance of existing standard machine learning techniques. In addition, the parameter sensitivity analysis indicates population as the most dominant contributing factor to the cause. The outcome of this research provides a robust framework in the form of a hybrid machine learning technique (IMM-ANN-PSO) in collaboration with MK test to model atmospheric CO 2 emission. No doubt, this approach can prove crucial in understanding the CO 2 emission trend and its contributing factors with greater conviction ultimately contributing to the creation of a safer environment.