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Demonstrates improved crop predictions using machine learning models in precision agriculture, indicating effective decision-making for farmers.
This research paper presents a smart crop prediction system using Random Forest and Machine Learning models. The system analyzes soil nutrients, temperature, humidity, pH, and rainfall to recommend suitable crops. The Random Forest model achieved 91.8% accuracy and outperformed Decision Tree, SVM, and Logistic Regression models. The project was deployed as a Flask web application for real-time crop recommendations in precision agriculture.
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T. Yashodha (2026) studied this question.
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