Climate Change Impact Analysis on Plantation

Agriculture industry is highly dependent on the climate. Change in temperature and climate can growth crop yields in some places. This research explores the repercussions of climate changes on global crop yields by employing machine learning techniques. The dataset spans across various regions and encompasses 28,242 records featuring seven key columns. Focusing on essential crops, such as corn and soybeans, this study leverages geospatial data to analyses planting and harvest dynamics over the period 2001-2021. Utilizing advanced machine learning models, including deep neural networks and reinforcement learning, the research seeks to predict and quantify the impacts of climate changes on crop yields on a global scale. The selected variables, such as average rainfall, pesticide usage, and temperature, contribute to a comprehensive analysis of the complex relationships between climate variations and crop productivity. By adopting a data-driven approach, this research aims to provide valuable insights for sustainable agricultural practices and adaptation strategies on a worldwide scale.