An effective crop recommendation method using machine learning techniques

The soil plays a vital role in agriculture, and soil testing serves as the initial step in determining the optimal nutrient levels for cultivating specific crops. Machine learning (ML) classification techniques can leverage soil nutrient data to recommend suitable crops. The Wrapper-PART-Grid approach, which incorporated crop recommendation data to suggest appropriate crops, was introduced in this paper. This hybrid method combined the grid search (GS) method for hyperparameter optimization, wrapper feature selection strategy, and the partial C4.5 decision tree (PART) classifier for crop recommendation. The proposed approach was compared with other ML techniques, including multilayer perceptron (MLP), instance-based learning with parameter k (IBk), C4.5 decision tree (CDT), and reduced error pruning (REP) tree. Evaluation metrics such as true positive rate, false positive rate, precision, recall, F1-score, root mean squared error (RMSE), and mean absolute error (MAE) were employed to assess these models. The suggested method demonstrated superior reliability, accuracy, and effectiveness compared to other ML models for crop advisory purposes. This method attained a remarkable accuracy rate of 99.31%, the highest among all the approaches considered. In this paper, a ML-based crop recommendation technique aimed at assisting farmers in enhancing their knowledge of cultivating appropriate crops. The technique not only seeks to reduce overall wastage but also aims to increase crop yield and improve crop quality.