The Role of Artificial Intelligence (AI) in Crop Pest and Disease Management: A Bibliometric Review
Keywords:
Artificial Intelligence; Deep Learning; Crop Disease; Pest Management; Bibliometric Analysis; Structural Fragmentation; Precision AgricultureAbstract
This bibliometric study systematically maps the intellectual, social, and conceptual structure of research concerning Artificial Intelligence (AI) applications for crop pest and disease management. Analyzing 3,391 documents sourced from Scopus up to 2025, the review confirms the field’s dramatic transformation, evidenced by exponential growth in scientific production since 2021, primarily driven by the maturity of Deep Learning (DL) methodologies. Utilizing network and thematic analysis, the study identifies two critical constraints: (1) severe structural fragmentation in global collaboration, with research heavily clustered in distinct Asian and Western hubs, impeding the development of universally generalizable AI models; and (2) a significant conceptual gap, where core themes like 'deep learning' and 'plant disease' are positioned as Basic Themes (high centrality, low density), indicating a lack of practical maturity and focus on real-world constraints such as model robustness and explainability (XAI). Despite these limitations, thematic evolution reveals a positive conceptual shift towards advanced localization techniques and the integration of AI within holistic Integrated Pest Management (IPM) strategies. We conclude that while the technological foundation is sound, future efforts must prioritize strategic cross-continental collaboration to diversify datasets and shift research focus towards achieving model robustness and XAI to maximize AI's transformative potential in securing global food production.
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