Machine Learning-based Weather Forecasting for Precision Agriculture: Model Development, Evaluation, and Predictive Insights

Weather forecasting performs a critical function in agricultural practices, influencing crop management, irrigation and pest manage choices. The paper examines the usage of coverage specialist strategies to refine climate forecasting in agriculture. Forecasting structures that analyze historical weather information, sensor readings, and outdoor climate forecasts can offer valuable insights into crop yields, most effective planting instances, and useful resource allocation. The paper discusses the hardware and software program interfaces required for data segregating, preprocessing, and model training. It emphasizes the significance of characteristic engineering, model evaluation, and actual-time choice guide systems. Ultimately, the study objectives to grow international food demand and empower farmers through correct and timely climate forecasting, promoting sustainable and green agricultural practices throughout a change in weather. The research project pioneers precision agriculture by using numerous system getting to know models along with Random Forest Regressor, Linear Regression, and Support Vector Machine to predict crop yields primarily based on complicated relationships with climate parameters, all backed by robust information control and feature importance analysis.