Resumen
This bibliometric study evaluated the scientific output on the use of artificial intelligence (AI) in the study of heavy metals in agriculture, aiming to identify research gaps and emerging trends. A search in Scopus retrieved 127 records; after applying inclusion and exclusion criteria, 58 were discarded and the remaining 69 were analyzed using Bibliometrix and VOSviewer. Graphs were generated to illustrate temporal evolution, country-level production, keyword co-occurrence, and thematic mapping. The publications show a high annual growth rate (42.86%), with China and India as leading contributors. The analysis revealed emerging research lines in fertilization, bioremediation, and intelligent monitoring, as well as gaps in food toxicology, input validation, rural training with AI, and the use of conversational interfaces such as ChatGPT for sustainable agriculture. These findings provide a strategic foundation to guide future interdisciplinary research in the agro-environmental field.
Citas
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