Paper Contents
Abstract
Predicting crop yield is essential for enhancing agricultural productivity. It helpsfarmers select the right crop based on factors like soil pH, rainfall, temperature, and humidity,thereby optimizing yield. Along with crop prediction, fertilizer recommendation is essential forbetter growth and productivity. This study introduces AgroAdvisor, an integrated system thatutilizes a hybrid approach by combining Random Forest with Extreme Gradient Boosting(RFXGB). RFXGB enhances feature processing, improving prediction accuracy. Theproposed model is evaluated against traditional machine learning methods using recall,precision, F-score, and accuracy, showing superior performance. Statistical analysis furtherconfirms RFXGBs effectiveness in boosting prediction accuracy
Copyright
Copyright © 2025 Mayur V Nikhare. This is an open access article distributed under the Creative Commons Attribution License.
