Modeling climate change adaptation strategies for agriculture through AI and big data

In this research, AI and Big Data analytics is integrated in the fit of climate change adaptation strategies for afforestation and deforestation agriculture. As weather patterns become increasingly difficult to predict and there is a growing need for the development of sustainable food systems, the need for data driven approaches to agriculture decisions becomes more and more pressing. For this study, four AI algorithms are applied to analyse climate, soil and crop yield datasets: “Random Forest, Support Vector Machine (SVM), Long Short Term Memory (LSTM) and Gradient Boosting.” First we aim to quantify the ways in which these algorithms can reliably predict optimal adaptation strategies, which include crop selection, irrigation scheduling and yield forecasting. Testing results show that LSTM is the best classification method for yield forecasting and yielded 92.3% prediction accuracy while Gradient Boosting followed with 89.7%, SVM with 85.2% and Random Forest with 87.5%. Additionally, the study compares to past works, and represents improved precision, recall and F1 score metrics for all the models. The results indicate that AI based models significantly improve agricultural adaptation decisions accuracy and reliability under climate change. Also, this research adds to the growing proof of the capability of big data frameworks to provide real-time, scalable, and local solutions for smart agriculture. The results will help establish robust climate resilient agricultural systems on a global scale.