Evaluating decision support systems for precision irrigation and water use efficiency
Uzair Ahmad, Ferdous Sohel
Abstract
• Analyzed studies that quantified water savings (50 %) and yield (20 %) via DSS tools. • Identified gaps in integrating UAV hyperspectral imaging with soil and weather data. • Evaluated AI models for predicting soil moisture and crop-specific water needs. • Reviewed IoT-sensors with satellite data for sub-field, real-time irrigation. • Proposed AI-driven DSS with low input needs and cost-efficient deployment. As global agricultural demands rise, optimizing irrigation management is essential for improving water use efficiency (WUE) across diverse cropping systems. This study evaluates the effectiveness of several Decision Support Systems (DSS)—specifically DSSAT, APSIM, CropWat, AquaCrop, GesCoN, and VegSyst—in enhancing irrigation practices tailored to the specific requirements of various crops. A systematic review was performed on these DSS, focusing on their methodologies for irrigation management. Key areas included each system's capabilities for integrating data from Wireless Sensor Networks (WSNs), remote sensing technologies, and critical environmental variables such as soil moisture levels, temperature fluctuations, and evapotranspiration rates. The adaptability of each system to crop-specific requirements was assessed, with an emphasis on data input needs, computational models, and the accuracy of their output recommendations. The review revealed significant advancements in integrating WSNs and remote sensing into DSS, which markedly improved irrigation scheduling accuracy. For instance, DSSAT and APSIM demonstrated water savings of 30–50 % by implementing adaptive irrigation schedules based on real-time soil moisture data, resulting in crop yield increases of up to 20 % under specific conditions. However, challenges remain, such as the high volume of data inputs required and the limited ability of these systems to account for spatial variability within fields. The integration of real-time data into the decision-making processes was identified as a crucial factor for enhancing irrigation efficiency. By combining innovative technologies with user-friendly interfaces, these systems can empower farmers to make informed, data-driven irrigation decisions. Future developments should prioritize simplifying data input processes and improving accessibility.