Crop Yield Prediction Based on Various Environmental Factors Using Machine Learning

The increasing impact of climate change and global warming highlights the urgent need for sustainable agriculture practices to ensure food security. This research focuses on utilizing machine learning techniques to accurately predict crop yield based on weather conditions. By analyzing historical weather data and crop yields, the proposed machine learning models provide valuable insights for farmers in selecting suitable crops for cultivation. This approach offers a more data-driven and reliable alternative to traditional methods that rely solely on farmers' experience and local knowledge. The study evaluates the performance of various machine learning algorithms, including Random Forest, XG Boost, and KNN. These models demonstrate exceptional accuracy in predicting crop yield, with values ranging from 92.12% to 97.56<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>. This research contributes to the advancement of sustainable agriculture by providing farmers with a powerful tool for optimizing crop selection and improving yields. By leveraging machine learning techniques, farmers can make informed decisions and mitigate the challenges posed by climate change.