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ABSTRACT
Introduction: In the agricultural domain, predicting crop yield and types plays a pivotal role in optimizing resource allocation, maximizing productivity, and ensuring food security. Leveraging machine learning (ML) algorithms like Random Forest, Gradient Boosting, and Logistic Regression offers promising avenues for accurate crop prediction. This abstract introduces InteliCrop, an ensemble model integrating these algorithms to enhance the precision of crop prediction.
Data Collection and Preprocessing: The foundation of Intelicrop lies in robust b data collection and preprocessing methodologies. Gathering and meticulously cleaning datasets that encompass various agronomic features, including soil quality, weather patterns, and historical crop yields. Employing feature engineering techniques to extract relevant attributes, ensuring the compatibility of data for model training.
Machine Learning Algorithms: Intelicrop utilizes a blend of three powerful ML algorithms to list :Random Forest, Gradient Boosting, and Logistic Regression. Random Forest employs decision trees and ensemble learning to handle large datasets and mitigate overfitting. Gradient Boosting sequentially builds weak learners, iteratively improving model accuracy. Logistic Regression, a linear classifier, assesses the probability of a specific crop based on input features.
Ensemble Model Construction: The strength of Intelicrop lies in combining the predictions from individual algorithms into an ensemble model. Aggregating the diverse predictions from Random Forest, Gradient Boosting, and Logistic Regression leverages their strengths to compensate for each other’s weaknesses, employing techniques such as bagging and boosting. Given that this fusion aims to produce a more robust and accurate crop prediction model.
Performance Evaluation and Validation: The performance of Intelicrop is rigorously assessed using metrics such as accuracy, precision, recall, and F1-score. Cross-validation techniques ensure both model generalizability and reliability across diverse datasets. Comparative analysis against baseline models and real-world testing validate the efficacy and practical applicability of Intelicrop in diverse agricultural settings.
Conclusion: Intelicrop represents a novel ensemble approach that harnesses the strengths of correspondingly Random Forest, Gradient Boosting, and Logistic Regression for precise crop prediction. Hence It’s potential impact spans optimizing farming practices, resource allocation, and contributing significantly to global food security.
