This paper provides an overview of the various applications of Artificial Intelligence (AI) in precision irrigation. It covers key research areas, methodologies, challenges, and future prospects in the field. The methodology is based on exploring how AI technologies are being used to optimize water management in agriculture and examines the growing body of research on the application of AI in irrigation systems. Deep investigation was conducted to explore how AI technologies can enhance water management in agriculture, leading to improved water management and crop yield in addition to resource efficiency. The paper discusses AI-based methods for monitoring soil conditions, weather forecasting, and real-time decision-making in irrigation. However, integration of AI systems with existing irrigation infrastructure and farming practices can be challenging, requiring significant investment in hardware and software.

This study aims to offer a comprehensive assessment of the impacts of policies and institutional arrangements on irrigation management performance. The case study, the Gezira Scheme, has witnessed a significant decrease in water management performance during recent decades. This situation led to several institutional changes in order to put the system on the right path. The main organizations involved in water management at the scheme are the Ministry of Irrigation & Water Resources (MOIWR), the Sudan Gezira Board (SGB), and the Water Users Associations (WUAs). Different combinations from these organizations were founded to manage the irrigation system. The evaluation of these organizations is based on the data of water supply and cultivated areas from 1970 to 2015. The measured data were compared with two methods: the empirical water order method (Indent) that considers the design criteria of the scheme, and the Crop Water Requirement (CWR) method. Results show that the MOIWR period was the most efficient era, with an average water surplus of 12% compared with the Indent value, while the most critical period (SGB & WUAs) occurred when the water supply increased by 80%. The other periods of the Irrigation Water Corporation (IWC), (SGB & MOIWR), and (WUAs & MOIWR) had witnessed an increase in water supply by 29%, 63%, and 67% respectively. Through these institutional changes, the percentage of excessive water supply jumped from 12% to 80%. Finally, the study provides general recommendations associated with institutional arrangements and policy adoption to improve irrigation system performance.