A Comparative Analysis of Decision Trees, Random Forests, and XGBoost for Enhanced Crop Recommendation

Agriculture is crucial to meeting food demand, and crop choices must improve productivity and sustainability. Machine Learning (ML) algorithms have been useful in this setting for recommending crops. This research analyses the current state of the art in crop recommender systems and how they might be improved via the use of ML methods. Effectively predicting what crops would thrive in a given environment is now possible using ML algorithms. Each model's strengths and drawbacks are highlighted in the comparative study, shedding light on how well or poorly they perform in crop recommendation tasks. Decision trees, random forests, and XGBoost are just a few examples of the prominent ML models used in crop recommendation systems. These models factor in a wide range of environmental and soil-related characteristics to determine which crops would thrive in a particular setting.