A Machine Learning–Driven IoT Architecture for Intelligent Irrigation and Fertigation in Horticultural Crops

Effective management of water and nutrients is critical to improving productivity and sustainability in horticultural crop production systems that are experiencing increased climate variability, and limitations in water and nutrient resources. This paper presents an Internet of Things (IoT) architecture driven by Machine Learning (ML) for intelligent irrigation and fertigation management that is specific to horticultural systems. The proposed framework integrates a real-time multi-sensor network of soil moisture, soil temperature, soil electrical conductivity (EC), soil pH, ambient humidity, and vapor-pressure deficit (VPD) sensors with a NodeMCU microcontroller cloud data infrastructure. A supervised learning model based on the Extreme Gradient Boosting (XGBoost) algorithm performs multi-class irrigation decision-making, placing water requirements in three categories: no irrigation, light irrigation, and heavy irrigation. The developed model achieved a testing accuracy of 95.0%, along with additional metrics that provided strong performance benchmarks including a macro-average F1-score of 0.94, and a ROC-AUC score of 0.97, showing high reliability for prediction at the field level. An anomaly detection module provides operational safety by identifying sensor drift, pipeline leakage, and actuator failure to entrain an automated fail-safe mode. The irrigation management system accommodates dynamically changing environmental dynamics by extending the data acquisition period and periodically retraining the model. The experimental research undertaken in agricultural fields of horticulture indicates increased water-use efficiency, decreased fertilizer consumption, and improved nutrient uniformity as opposed to standard irrigation based on rules. Thus, this study illustrates that the coupling of IoT sensing technology with intelligent machine-learning control is possibly disruptive for the future of precision sustainable horticulture.