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Artificial intelligence and machine learning are transforming the agriculture sector by enabling higher levels of efficiency and productivity. Linear regression, decision trees, random forests, support vector machines, and neural networks are some of the algorithms being used to estimate agricultural production. These algorithms can provide a baseline model, a clear and visual representation, improved robustness and accuracy, an optimal hyperplane for grouping data, and the ability to capture intricate and nonlinear patterns in the data.
