Machine Learning-Based Weather Prediction for Agricultural Planning

Effective agricultural planning depends on precise weather forecasting, especially in areas susceptible to climate change. This paper suggests a machine learning-based method to forecast weather conditions utilizing a mix of meteorological information, satellite pictures, along with real-time sensor inputs. Starting with the collection of multiple sources of data such as humidity, wind speed, temperature, as well as soil parameters, the approach covers the whole pipeline. Preprocessing techniques including mean imputation for values that are missing, Z-score normalization, and PCA (principal component analysis) dimensionality reduction follow. The system investigates many prediction models including LSTM (Long Short-Term Memory) networks, Support Vector Regression (the SVR), as well Random Forest Regression. Eighty percent of the data is used to optimize model training; K-fold cross-validation then validates it to reduce overfitting. Metrics include Mean Squared Error (MSE), root mean square errors (the RMSE), as well as Mean Absolute Error (MAE) are used to evaluate performance. Key meteorological factors are then predicted using the trained model; they are then included into agricultural decision-making systems like pest management, irrigation planning, and crop selection. The method also enables continual model improvement by always updating with fresh data to preserve accuracy and flexibility. By use of data-driven weather forecasting, this all-encompassing system shows a scalable and smart approach to improving agricultural output.

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