Predictive Modeling for Crop Recommendation Using Machine Learning Techniques for Agricultural Optimization

This work aims to develop a predictive model for recommended crop using machine learning techniques. The dataset, containing various soil attributes, was initially loaded and preprocessed by extracting latitude and longitude information from the location data, removing unnecessary columns, and one-hot encoding categorical variables such as soil type. The independent variables were defined as all relevant features except for recommended crops, which served as the dependent variable. The dataset was then split into training and testing sets, and the features were scaled to ensure consistency. Several machine learning models were trained, including random forest classifier, Support Vector Machine (SVM) regressor, neural network, decision tree and multinomial naive bayes. Each model's performance was evaluated based on metrics like accuracy, precision, recall, F1-score. The models were compared to determine the best performer. This work provides a comprehensive approach to suggest recommended crop based on the minimal environmental parameters, which is crucial for efficient agricultural practices and resource management.