Variable Rate Irrigation Through Digital Agriculture for Sustainable Water Management: A Meta Review on Current Challenges and Future Directions

Efficient agricultural water management is essential due to increasing pressure on global water resources. To support environmentally sustainable farming water management, a detailed review of existing research on data-driven irrigation systems is conducted. The review paper reviews present advancements, challenges, and future directions based on extensive peer-reviewed studies on variable-rate irrigation (VRI), machine learning and artificial intelligence (AI), deep learning techniques, smart sensing technologies, state-of-the-art control systems, and coupled frameworks. To ensure transparency and reproducibility, studies were selected through a systematic search of major research databases (such as Dimensions AI, Web of Science, and Scopus) using key keywords. The inclusion criteria required that studies be peer-reviewed, published in English between 2000 and 2023, and specifically address precision irrigation techniques, control strategies, or implementation methods relevant to VRI. Conference proceedings and high-impact journal articles were prioritized. This review focuses on three central components of precision irrigation: (1) development of prescription maps, (2) innovative control strategies, and (3) realistic implementation of VRI systems. To improve the efficiency and flexibility of modern irrigation systems, the paper identifies emerging knowledge gaps and proposes innovative pathways to address them. The study describes a new methodology through a case study on developing irrigation prescription maps. Precision irrigation, especially when supported by VRI and state-of-the-art AI technologies, has shown considerable potential to improve water productivity. This approach assists researchers, practitioners, and stakeholders in designing, comparing, and executing sustainable irrigation strategies.