Remote Sensing Applications in Medicinal Plant Monitoring and Quality Assessment: A Review

Abstract

As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources.

Plant phenotyping relevance

植物の成長・ストレス・品質などの状態を対象に、リモートセンシングのプラットフォーム、センサー、解析手法を体系的にレビューしており、植物フェノタイピング手法が中心です。

Code and data availability

This is a review article with no original phenotyping measurements or analysis code. The Data Availability Statement says 'No new data were created for this article.' The only supplement (Table S1) is a summary of cited literature, not a paper-specific dataset, image collection, or code asset. The market report URL is

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