Predictive Crop Selection Using Data-Driven Machine Learning Models

With the growing need for sustainable agricultural practices, the integration of intelligent systems in farming has become increasingly essential. Crop selection, a critical decision for farmers, is often influenced by numerous environmental and soil-related factors. In this context, the present study focuses on the development of a machine learning-based crop recommendation system aimed at improving the decision-making process for crop cultivation. The system analyses various agronomic features including soil nutrients nitrogen (N), phosphorus (P), potassium (K) along with temperature, humidity, pH level, and rainfall to suggest the most appropriate crop for a given set of conditions. A dataset containing these parameters was pre-processed to remove inconsistencies and scaled using Min-Max normalization and standardization to enhance model performance. Several classification algorithms were implemented and evaluated, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Gaussian Naïve Bayes, Random Forest, Gradient Boosting, AdaBoost, Bagging Classifier, and Extra Trees. These models were trained and tested using an 80-20 split of the dataset, and their performance was assessed based on accuracy metrics. Among all tested models, the Random Forest classifier emerged as the most reliable, delivering the highest prediction accuracy due to its ability to handle high-dimensional data and reduce overfitting. The system also includes a functional interface allowing users to input real-time environmental values and receive instant crop recommendations. This project demonstrates how machine learning can be effectively leveraged to support agricultural decisions, reduce crop failure risks, and enhance yield potential. By offering a data-driven approach to crop planning, the system contributes to more efficient land use, resource optimization, and long-term sustainability in agriculture.