Trend analysis of the application of multispectral technology in plant yield prediction: a bibliometric visualization analysis (2003–2024)

Abstract

Multispectral imaging technology uses sensors capable of detecting spectral information across various wavelength ranges to acquire multi-channel target data. This enables researchers to collect comprehensive biological information about the observed objects or areas, including their physical and chemical characteristics. Spectral technology is widely applied in agriculture for collecting crop information and predicting yield. Over the past decade, multispectral image acquisition and information extraction from plants have provided rich data resources for scientific research, facilitating a deeper understanding of plant growth mechanisms and ecosystem function. This article presents a bibliometric analysis of the relationship between multispectral imaging and crop yield prediction, reviewing past studies and forecasting future research trends. Through comprehensive analysis, we identified that research using multispectral technology for crop yield prediction primarily focuses on key areas, such as chlorophyll content, remote sensing, convolutional neural networks (CNNs), and machine learning. Cluster and co-citation analyses revealed the developmental trajectory of multispectral yield estimation. Our bibliometric approach offers a novel perspective to understand the current status of multispectral technology in agricultural applications. This methodology helps new researchers quickly familiarize themselves with the field’s knowledge and gain a more precise understanding of development trends and research hotspots in the domain of multispectral technology for agricultural yield estimation.

Plant phenotyping relevance

作物収量予測に用いるマルチスペクトル技術の研究動向を対象とした書誌計量レビューであり、植物形質（収量）の計測・推定手法分野を方法論的にレビューしている。

Code and data availability

This is a bibliometric review (VOSviewer/CiteSpace analysis of Web of Science records) with no plant-phenotyping measurements of its own. The supplied blocks contain no public phenotype/trait datasets, plant images, sensor data, author analysis code, or trained models, and no data or code availability statement with a

No evidence-backed public reproduction asset is currently recorded.