An IoT-driven predictive analytics framework for dynamic irrigation optimization in tomato cultivation

Area of Science:

• Agricultural Engineering
• Data Science
• Environmental Science

Background:

• Global food security and water scarcity are critical challenges.
• Efficient irrigation is vital for sustainable agriculture and crop resilience.

Purpose of the Study:

• To develop an Internet of Things (IoT)-driven predictive analytics framework for dynamic irrigation optimization in tomato cultivation.
• To accurately estimate daily water requirements, minimizing water consumption while maintaining soil health.

Main Methods:

• Leveraged a comprehensive dataset from multi-sensor IoT deployments (environmental and soil parameters).
• Applied rigorous data preprocessing and feature engineering, including Growing Degree Days (GDD).
• Developed and validated a two-part eXtreme Gradient Boosting (XGBoost) regression model.

Main Results:

• The XGBoost model achieved a high R² of 0.9476 for predicting daily water volume.
• Demonstrated a potential water saving of 50.84% in simulated dynamic optimization.
• Identified optimal water levels under varying conditions with high accuracy.

Conclusions:

• The framework provides a sophisticated intelligence layer for irrigation scheduling.
• Offers data-driven insights for effective precision irrigation strategies.
• Empowers farmers to reduce water waste and promote sustainable smart agriculture.