First Author: LI Yonglin
Corresponding Author: WU Wenyong
Journal: Irrigation and Drainage

Abstract

To realize intelligent decision-making for water and fertilizer management in drip irrigation, and to make full use of current fertigation theories to improve efficiency, this study developed three irrigation and fertilization decision-making methods based on evapotranspiration (ET, T1), canopy temperature (T2), and soil moisture and temperature (T3). These methods were integrated into an automatic control system.

Using greenhouse tomatoes as the experimental subject, a smart fertigation control system was designed, which included real-time monitoring of microclimatic conditions, soil moisture, and irrigation system operation, as well as an automatic control module for implementing fertigation decision-making strategies. Three irrigation modes were tested.

Results showed that the automatic control system maintained irrigation volumes and fertigation application rates with average errors of only 1.1% and 0.8%, respectively. The system operated stably, and the precision of decision-making schemes was verified. Compared with conventional control, tomato yields under the three intelligent decision-making methods increased by 7.8%, 11.7%, and 6.7%, respectively, while irrigation and fertilizer consumption decreased, significantly improving water- and fertilizer-use efficiency.

Although ET- and canopy temperature-based methods showed lower accuracy in the measurement of soil water content compared to field measurements, their overall trends were consistent with actual conditions, confirming the reliability of these strategies when supplemented with experimental data. This study provides valuable insights for the development of intelligent fertigation decision-making and control systems, contributing to further improvements in water-use efficiency.

Keywords: Drip irrigation; Environmental monitoring; Fertigation; Intelligent decision-making model