Enhancing precision agriculture through cloud based transformative crop recommendation model.

Area of Science:

• Agricultural Science
• Computer Science
• Data Science

Background:

• Modern agriculture increasingly depends on technology to enhance food production quality and quantity.
• Traditional crop recommendation methods often lack real-time data integration and personalization.

Purpose of the Study:

• To introduce a novel Transformative Crop Recommendation Model (TCRM) for personalized crop suggestions.
• To leverage machine learning and cloud platforms for optimizing agricultural practices.

Main Methods:

• Developed a Transformative Crop Recommendation Model (TCRM) integrating real-time environmental and agronomic data.
• Utilized advanced machine learning algorithms and cloud computing infrastructure.
• Incorporated SMS alerts for remote farmer communication.

Main Results:

• The TCRM model achieved 94% accuracy, 94.46% precision, 94% recall, and an F1 score of 93.97%.
• Fivefold cross-validation demonstrated a high model performance with a score of 97.67%.
• TCRM outperformed baseline algorithms including Logistic Regression, KNN, and AdaBoost.

Conclusions:

• The TCRM model significantly improves precision farming by providing actionable, region-specific insights.
• It enhances farmer profitability and promotes sustainable agricultural practices through optimized resource management and yield boosting.
• The model demonstrates potential for increasing agricultural efficiency and sustainability.