click here to download the abstract of random forest algorithm
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ABSTRACT–
We provide abstract for random forest algorithm in this paper.
Introduction: In modern agriculture, predicting crop yields plays a crucial role in optimizing resource utilization and ensuring food security.
Methodology: The research employs a dataset comprising historical agricultural data, including climate conditions, soil properties, and crop management practices. This applied to train the model, utilizing an ensemble of decision trees to capture intricate relationships between various factors influencing crop yields.
Results: The study showcases the effectiveness of the Random Forest algorithm in accurately predicting crop yields. The results indicate a significant improvement in prediction accuracy compared to traditional methods.
Implications: The findings of this research have practical implications for farmers, policymakers, and agribusinesses. Accurate crop yield predictions empower farmers to make informed decisions regarding crop selection, resource allocation, and risk management. Policymakers can use this information to implement targeted interventions and support agricultural sustainability.
Conclusion: In conclusion, the application in crop yield prediction demonstrates its potential to revolutionize agricultural decision-making. The study provides valuable insights into leveraging advanced machine learning techniques for enhancing productivity and sustainability in the ever-evolving field of agriculture.
