Mapping the Global Knowledge Structure of Artificial Intelligence Applications in Horticultural Production: A Bibliometric Analysis

Artificial intelligence (AI) is increasingly important for precision horticulture, yet research on its applications remains fragmented across crops, technologies, and production challenges. This study uses bibliometric analysis to map the global knowledge structure of AI applications in horticultural production and assess Africa’s position within this research landscape. Bibliographic records were retrieved from the Dimensions database using artificial-intelligence terms (”artificial intelligence,” ”machine learning,” ”deep learning,” and ”computer vision”) combined with horticulture-related terms, yielding 7,519 documents published across 1,070 sources between 2020 and 2025. Using the bibliometrix R package, the field showed rapid growth, with an annual growth rate of 54.27%, driven primarily by China and India, followed by the United States, Italy, and Australia. Agronomy, Frontiers in Plant Science, and Agriculture were the most productive sources, while China Agricultural University and Northwest Agriculture and Forestry University led institutional output. Co-word analysis identified three dominant research streams: (i) AI and precision diagnostics, (ii) crop, soil, and water management, and (iii) sustainable and climate-smart agricultural systems. International co-authorship accounted for 27.93% of publications. Africa contributed only 8.09% of global country-level output, with Egypt, Nigeria, South Africa, Ethiopia, and Morocco as the leading contributors, while 16 African countries were absent from the dataset. The findings highlight a substantial gap between Africa’s research contribution and its horticultural and food-security needs, underscoring the need for targeted funding, local datasets, interdisciplinary capacity building, and stronger South–South research collaboration to advance AI-enabled horticulture across the continent.