Automated Machine Learning for Agricultural Decision Support: A TPOT-based Crop Prediction System

Agriculture is an important sector that depends heavily on accurate decision-making to maximize crop selection and enhances productivity. The conventional machine learning (ML) methods have proven effective in crop prediction, but it needs manual intervention for model selection, feature engineering, and hyperparameter optimization. AutoML as a useful tool for decision support system improvement in agriculture, providing an efficient solution for precision farming and sustainable crop management. The TPOT (Tree-based Pipeline Optimization Tool) framework, which is an application of Automated Machine Learning (AutoML), is used in this study to create an efficient and successful crop prediction system. The outcomes are compared with those of traditional machine learning models.