Applied Sciences, Год журнала: 2025, Номер 15(8), С. 4228 - 4228

Опубликована: Апрель 11, 2025

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) data-driven insights, optimizing usage. In this study, from an initial pool 48 papers retrieved well-known databases such as Scopus Web Science, etc., a rigorous eligibility criterion was applied, narrowing focus to 11 pertinent studies. highlights major disciplines where technology is being applied: hydroponics, aquaponics, vertical farming, irrigation. Additionally, literature identifies two key sub-applications within these disciplines: simulation prediction quality soil water. also types maturity levels concepts applications. Based on their current implementation, in agriculture can be categorized into functional types: monitoring DTs, which emphasize response environmental control, enable proactive irrigation management forecasting. techniques used framework were identified based These findings underscore transformative role that play enhancing efficiency sustainability Despite technological advancements, challenges remain, including data integration, scalability, cost barriers. Further studies should conducted explore issues practical farming environments.

Язык: Английский