AI & IoT-based Irrigation Publication Dataset (2006-2025) - Bibliometric Research Corpus

Overview

The AI & IoT-based Irrigation Publication Dataset (2006-2025) published on Mendeley Data in March 2025 provides a curated collection of bibliographic records for research mapping and meta-analysis of smart irrigation technologies.

Data Sources

• Records exported from Scopus and Web of Science (WoS) databases, the two leading scholarly publication indexing services.
• Coverage period: 2006 to 2025 (nearly 20 years of research evolution).
• Search queries focused on intersections of artificial intelligence, Internet of Things, and irrigation systems.

Dataset Contents

• Bibliographic metadata: Authors, titles, abstracts, keywords, publication years, journals/conferences, citations, affiliations.
• Publication trends: Temporal distribution showing growth in AI-IoT irrigation research over two decades.
• Geographic distribution: Author affiliations indicating leading countries and institutions in the field.
• Topical clustering: Keywords and abstracts enabling content analysis and thematic mapping.

Intended Uses

• Bibliometric mapping and visualization: Using tools like VOSviewer, CiteSpace, or Bibliometrix to create co-authorship, co-citation, and keyword co-occurrence networks.
• Systematic literature reviews: Identifying key papers, influential authors, and foundational works in AI-IoT irrigation.
• Research trend analysis: Tracking the evolution of topics, methods, and applications over time.
• Gap identification: Discovering underexplored areas and emerging research directions.
• Policy and funding insights: Understanding global research investment patterns and collaborative networks.

Research Applications

• Supporting PhD students and researchers conducting literature reviews in precision agriculture and smart irrigation.
• Informing funding agencies and policymakers about research priorities and capacity in different regions.
• Guiding journal editors and conference organizers in understanding field development and hot topics.
• Facilitating interdisciplinary collaboration by mapping connections between AI, IoT, and agricultural engineering communities.