Predicting Weather Conditions Using Machine Learning for Improving Crop Production

Abstract

Weather conditions play an important role in crop production and agricultural decision-making. Accurate prediction of weather conditions can help farmers make informed choices regarding planting, irrigation, and pest management, ultimately improving crop yield and reducing resource wastage. In recent years, machine learning techniques have shown promise in weather prediction due to their ability to analyze large amounts of data and detect complex patterns. This research aims to develop a machine learning model for predicting weather conditions to enhance crop production. The study utilizes historical weather data, including temperature, precipitation, wind speed, and humidity, obtained from various meteorological sources. Feature engineering techniques are applied to extract relevant information from the data, and preprocessing methods are employed to handle missing values and outliers. Several machines learning algorithms, including regression models, decision trees, and ensemble methods, are employed to train and evaluate the predictive models.