• Expert systems integrate sensor data for improved date palm monitoring. • 42 studies analyzed highlight AI’s potential in date palm agriculture. • Data quality and system adaptability remain key challenges in AI use. • Future focus: robust AI models and diverse datasets for better results. Date palms are an essential agricultural asset in arid and semi-arid areas, considerably enhancing local economies and food security. Recent research has examined several facets of date palm production, including genetic diversity, water management, and disease detection, underscoring the need to understand these elements to improve productivity and sustainability. This systematic review seeks to consolidate research on date palm farming, emphasizing techniques such as Artificial Intelligence (AI) and expert systems in date palm production. The search was conducted across five databases to identify relevant studies published from 2020 to October 2024. The non-English, non-peer-reviewed, and full-text-unavailable papers were excluded. A total of 39 papers covering the date palm in AI and expert systems were included in the review. Though scalability and real-time adaptation were limited, expert systems showed promise in combining sensor data for monitoring and offering valuable insights. While AI models achieved excellent accuracy in illness detection and classification tasks, they struggled to generalize to new datasets and to apply in real-world settings. The detection and management of date palm cultivation could be significantly improved by expert systems and AI models. Nonetheless, its practical implementation is constrained by issues related to data quality, adaptability, and integrating systems. Future research should focus on developing robust, adaptable structures and integrating diverse datasets to address these constraints.
A. M. Mutawa (Fri,) studied this question.
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