MACHINE LEARNING PREDICTION FOR CROP SELECTION USING A RANDOM FOREST CLASSIFIER

Abstract: This work proposes a machine learning crop recommendation system that supports enhanced agricultural decision making. The model employs crucial factors such as soil nutrients (nitrogen, phosphorus, potassium), pH, TMP, HUM, and RF in order to predict optimal crops to plant. We compared five classifiers: Logistic Regression, Support Vector Classification (SVC), Multilayer Perceptron (MLP), Random Forest and Decision Tree. Among these models, RF performed the best with the highest accuracy of 99.27%, demonstrating good performance on challenging agricultural data. The model was released as an interactive web app developed in Streamlit, which generates a real-time forecast for farmers to choose a crop. This study has shown the superiority of ensemble and nonlinear machine learning models over linear type in agriculture. It offers a potential scaling tool toward enhancing crop yield and sustainability.

Keywords: Machine Learning, Crop Selection System, Smart Agriculture, Random Forest Classifier, Performance Analysis, Scalability, Streamlit Web Application, Prediction Accuracy.