Abstract
Food scarcity will be a threatening problem in front of the global civilization due to huge growth in world population and reduce in world agricultural land covers. Agriculture depends on several factors like climate, soil conditions, irrigation, fertilization, condition of pests. The increase in carbon footprint due to civilization adversely affects the worldwide climate which causes unexpected floods, droughts and increase in pests directly affects the productivity and quality of agricultural products. We can increase the productivity of agricultural sector by analyzing and predicting the data of external parameters like carbon footprint, rainfall information, moisture information, soil information by predicting flood, drought, pest movement and other factors. In this article, we tried to perform the prediction of rainfall and carbon-footprint and used regression analysis for finding the correlation between Indian agricultural data containing carbon footprint and rainfall over Indian geography which can helps to increase the indian agricultural product.
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Hazra, S., Mondal, K.C. (2024). Regression Analysis for Finding Correlation on Indian Agricultural Data. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_11
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