Machine Learning-based Strategies for Crop Assessment in Diverse Districts of Uttarakhand

Crop prediction has historically been influenced by farmers’ needs, but the impact of climate change on crop yields has made traditional methods unreliable. Factors such as soil quality and environmental conditions are no longer sufficient for determining which crops to grow. Manual predictions have proven ineffective, prompting the need for accurate crop prediction methods. Machine learning has emerged as a crucial tool for this purpose, considering various factors like soil composition, geographical location, and climate conditions. Selecting the appropriate crop based on these attributes is essential for accurate predictions. This paper examines multi-class crop prediction models employing machine learning techniques. Specifically, Xgboost and Random Forest classifiers were utilized in our analysis. While both models demonstrate good accuracy, The findings of this study indicate that both Random Forest and Xgboost models achieve relatively high accuracy rates in predicting crops from the dataset. Specifically, the Xgboost model outperforms the Random Forest model slightly, with an accuracy of 83.25% compared to 82.5%.