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

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.

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Publication Details

Journal
Sensors
Published
2026-04-16
DOI
https://doi.org/10.3390/s26082465
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Yingpin Yang, Guanqiao Chen, Xumei Wang, Ziying Wang et al.
Sensors
Remote Sensing in Agriculture
article

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

Yingpin Yang, Guanqiao Chen, Xumei Wang, Ziying Wang, Jinnian Wang, Yuxin Fan, Jinping Ji
article en

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.

SensorsVol. 26(8)
Center For Remote Sensing (United States) (US), Xi'an Botanical Garden of Shaanxi Province (CN), State Key Laboratory of Remote Sensing Science (CN), New York University (US), Xi'an Jiaotong University (CN)
Shaanxi Administration of Traditional Chinese Medicine, National Natural Science Foundation of China, Shaanxi Academy of Sciences, Guangzhou Municipal Science and Technology Bureau
Responsible consumption and production
Openalex Percentile: Top 21%
Remote Sensing in Agriculture
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