An Analysis of Crop Recommendation Systems Employing Diverse Machine Learning Methodologies

For the purpose of assisting farmers in making knowledgeable choices on crop management, this research suggests a crop recommendation system based on machine learning algorithms. To assess soil data and suggest the optimal crop management practices to farmers, the system makes use of Decision Tree, Naive Bayes, KNN, Random Forest, and XG-Boost. The research discovered that agricultural yields may be reliably predicted using machine learning algorithms, and these algorithms can also recommend the best crop management techniques, resulting in higher crop output at a reduced cost. The suggested method presents a possible answer to the problems the agriculture sector faces, such as population growth and changing dietary tastes.