Impact of Climate Change on Food Agriculture

Abstract

The escalating impact of climate change on food agriculture necessitates advanced analytical approaches for accurate forecasting and mitigation strategies. This study employs Stacked Long Short-Term Memory (LSTM) networks, a sophisticated variant of Recurrent Neural Networks (RNNs), to analyze and predict the influence of climate change on agricultural outputs. Stacked LSTMs, known for their efficacy in processing sequential and time-series data, are utilized to decipher the complex interdependencies between various climatic factors and agricultural productivity