Optimization of Irrigation in Open-Field Fruit Orchards Using an Intelligent Precision Control System: A Case Study of Durian

Abstract

Precision irrigation systems (PIS) are essential for optimizing water use, crop yield, and fruit quality. Estimating crop water requirements has become increasingly complex due to growing variability in weather patterns, which increases the risk of irrigation mismanagement. Conventional PIS often rely on fixed schedules or pre-determined water quantities. While some offer decision-support capabilities, they typically require human intervention and lack the ability to control irrigation. To address these issues, an intelligent precision irrigation system (IPIS) was developed. The IPIS utilizes real-time meteorological data collected from local sensors and implements advanced control algorithms based on the Penman-Monteith evapotranspiration method to optimize water delivery. A four-month field experiment with 166 irrigation events conducted in a durian orchard in Rayong, Thailand, demonstrated the system's ability to dynamically adjust irrigation schedules and water volumes in response to fluctuating weather conditions while maintaining optimal soil moisture levels. This resulted in significantly improved irrigation accuracy and crop water use efficiency. The findings suggest that incorporating machine learning and artificial intelligence in future iterations could further enhance the system’s adaptability, autonomous operation, and predictive capacity, advancing its application in precision agriculture.