Harnessing Machine Learning-Based Classification Techniques to Optimize Crop and Fertilizer Recommendations

With machine learning, agriculture which is essential to the world’s food security is experiencing a revolutionary change. Using methods such as XG Boost, Support Vector Machine (SVM), and Decision Tree, this research investigates three important agricultural domains: crop prediction, fertilizer management, and crop disease diagnosis. Accurate crop forecast that maximizes resource allocation with an average accuracy of 94%. Fertilizer management increases crop quality by 15%, lowers expenses by 20%, and increases output by 25% when it is based on soil parameters, historical data, and meteorological considerations. With XG Boost, a Crop Recommendation System that takes into account the specific climatic circumstances of the area provides customized guidance on crop selection and fertilizer application, guaranteeing a 99% average recommendation accuracy. Machine learning can revolutionize Indian agriculture, promote informed decision-making, and increase agricultural productivity while maintaining traditional methods. To fully realize this promise, more study on improving accessibility is essential.