<![CDATA[Background: Artificial intelligence (AI) is steadily changing the agricultural systems around the world. However, its adoption remains uneven between developed and developing economies, creating significant inequality in productivity, technological access and food security. Aim: This systematic review determines the adaptation of AI in agriculture across Australia, South Korea, Indonesia and Pakistan. Prior systematic reviews of AI in agriculture have largely examined technical performance within single countries or technology categories, without directly comparing adoption pathways across countries at different stages of economic development. This review addresses that gap. Methods: This study addresses a critical research gap by following PRISMA 2020 guidelines. A systematic search of the Scopus database showed 940 records. Out of these, only 47 peer-reviewed studies (2015-2024) were included after screening for qualitative synthesis and thematic analysis. Extracted data were coded and grouped into four themes aligned with the review objectives, and the percentages reported below reflect the proportion of the 47 included studies in which each theme, technology, barrier or benefit was identified during coding. Results: The most prominently reported AI approaches in the studies were Machine learning (51.1%), precision farming (48.9%) and computer vision (46.8%). Developed economies demonstrated advanced integration of precision agriculture and automation. Developing economies mainly use AI for disease detection, crop monitoring and yield forecasting. The most common challenges reported were the high cost of implementation (38.3%), limited infrastructure (36.2%) and poor data quality (31.9%). The most frequently reported benefits were improved crop yields (61.7%), better resource use efficiency (51.1%) and more effective disease management (46.8%). These percentages show how many studies reported each benefit, but they do not indicate the actual level of improvement achieved. Conclusion: Overall, the findings suggest that the adoption of AI depends not only on the availability of technology but also on infrastructure readiness, supportive policies and strong institutional capacity. This review highlights the need for context-specific strategies, greater investment in rural digital infrastructure and inclusive innovation frameworks to support fair and sustainable agricultural development.]]>
