Nutritional Diagnosis in Oil Palm Using UAV-Based Multispectral Imagery: A Global Bibliometric Analysis, 2005–2024

Abstract

This study analyzes trends in research productivity, thematic evolution, and knowledge gaps concerning the use of unmanned aerial vehicles (UAVs) equipped with multispectral sensors for nutrient diagnosis in oil palm plantations. a bibliometric analysis was conducted on publications indexed in Web of Science and Scopus during the period 2005–2024, following in four phases: 1) a qualitative exploratory review of 50 scientific articles to identify search terms. 2) a comprehensive systematic search using 83 search equations, without restrictions on language, document type, or area. 3) temporal delimitation through frequency analysis. and 4) cross-validation using thematic relevance mapping. Extracted data included title, authors, keywords, abstract, year, citations, and country.

The bibliometric analysis reveals a substantial knowledge gap in applying multispectral UAV technology to estimate nutrient concentrations in oil palm. Despite the exponential growth in UAV research (2017-2024) and plant nutrition studies (2005-2024), the intersection of these fields within oil palm remains largely underdeveloped, with only five publications identified. Research is concentrated in Malaysia (30%) which aligns with its status as the world's leading producer. This gap represents both a challenge and an opportunity for future research, particularly in the developing of standardized protocols for non-destructive nutrient diagnosis that could help address production stagnation in plantations.