Machine Learning and Mathematical Modeling in Agricultural Development

Modern agricultural practices have experienced a revolution through the combination of machine learning (ML) and applied mathematics which enables data-driven decision-making and potential for modern agriculture. The research investigates a crop recommendation system which uses supervised ML models that analyze enriched data containing nitrogen (N), phosphorus (P), potassium (K), pH, temperature, humidity, and rainfall information. The research aims to enhance precision agriculture through recommendations for optimal crops that will produce maximum yields and financial gains for farmers. The analysis employed six machine learning models which included Decision Trees, Random Forest, Gaussian Naive Bayes, Logistic Regression, XGBoost and Support Vector Machines (SVM) to evaluate their performance through cross-validation and classification metrics. The research implements learning methods and optimization techniques to achieve higher than 99% accuracy when classifying 11 different crop types. The study employed exploratory data analysis together with statistical methods to check for outliers and establish data reliability. ML models supported by mathematical and statistical analysis effectively analyze agro-environmental data to deliver dependable crop recommendations according to the research findings. The research results in substantial benefits for data-driven agricultural planning while promoting sustainability and delivering useful information to farmers.