Optimizing Fruit Crop Recommendations via Soil Analysis with XGBoost and Supervised Learning Techniques

Abstract

The importance of soil composition in determining suitable crops selection cannot be emphasized enough in the quest for increasing precision agriculture. The presence of soil components such as pH, nitrogen, phosphorus, and potassium levels significantly affect the growth and productivity of crops. Building upon this, the present work provides a cohesive strategy that merges the powerful XGBoost algorithm with conventional supervised learning techniques to improve crops recommendation systems. The study systematically utilizes machine learning techniques to surpass the limitations of conventional heuristic-based methods by examining the complex relationships between soil variables and crops performance. The research employs a large dataset including many soil properties to perform a thorough comparison analysis of numerous well-known machine learning algorithms, such as Decision Trees, SVM, Logistic Regression, Random Forest Naive Bayes and XGBoost. The findings evaluated is based on four important performance measures - accuracy, recall, precision, and F1-score - clearly demonstrate the better performance of the XGBoost model. With exceptional accuracy (99.31%), precision (100%), recall (99%), and an F1-score (99%), this system surpasses its competitors in precisely forecasting and proposing the best crops based on soil composition, demonstrating its unmatched effectiveness. The findings not only confirm the strong capability of XGBoost in managing intricate prediction tasks, but also emphasize its potential in promoting more sustainable and productive farming methods. The study's results have important implications for the creation of intelligent agronomic decision-making tools. These tools will use data to help farmers and agronomists achieve higher crops yields that are customized to the unique requirements of their soil conditions.