Random Forest-Powered Weather Classification for Smarter Agricultural Decisions

Agriculture has a big impact by weather patterns, which immediately affect crop growth, crop yield, and environmental sustainability. Conventional weather prediction models often affect the accuracy and fail to capture delicate weather patterns, leading to inadequate decisions in farming practices. Several methods, including statistical models and simpler machine farming techniques, have been tried for weather forecasting and classification. However, these models often struggle with scalability, large datasets, and complicated features. This study proposes an enhanced weather classification model using the Random Forest Algorithm, an accurate ensemble machine learning technique known for its high accuracy and ability to work with complex datasets. The Random Forest-Based weather classification model demonstrated a significant better in accuracy compared to existing methods, achieving a classification accuracy of over 90% on benchmark datasets.