Enhancing Crop Yield Prediction Accuracy in Precision Agriculture: A Comparative Analysis

In order to optimize crop yields and resource use, crop suggestion systems represent a significant development in contemporary agriculture. A comprehensive Crop Recommendation System has been created in this study to help farmers choose the right crop based on a variety of soil and environmental characteristics. To try to boost crop yields and agricultural productivity, our method makes use of deep learning (DL) and machine learning (ML) models. Information for training and evaluation was sourced from the dataset repository. The eight most crucial elements are included, Random forest, KNN, Decision tree, Logistic Regression, IBGM, Bagging Classifier, Gradient Boost, AdaBoost Classifier, RNN, Naive Bayes, LSTM, XGBoost, SVM, and the Stacking Ensemble model are a few of the deep learning and machine learning models employed in this research. Based on performance evaluation, the best models are the Random Forest and the Stacking Ensemble Model., Other models performed admirably as well, with accuracy levels varying. Even with its little lag, SVM managed an impressive accuracy rate. Additionally, parameter tuning for Stacking Ensemble Model and XGBoost is part of the research. Through parameter tuning, the Stacking Ensemble Model outperforms XGBoost in terms of Accuracy. The study’s conclusions demonstrate the value of deep learning and machine learning models in crop recommendation systems, and the Stacking Ensemble Model is a prime example of the model’s commitment to accurate and useful crop recommendations. Precision agriculture benefits from this research study’s reliable properties that improve crop selection depending on soil and environmental factors.