UAV mounted spectrometry camera solution for detecting illicit cannabis and ephedra plantations

Abstract Background There is a noticeable threat to the law enforcement posed by the proliferation of the cultivation of drugs around the world. One of the most popular illegal cultivated plants are Cannabis sativa and Ephedra sinica. This paper presents a new way of detecting such crops with hyperspectral image application using unmanned aerial vehicles (UAVs) to find such crops in inaccessible mountainous lands. Techniques It was a field study within the frames of UNODC mission in Kyrgyzstan by using a fixed-wing UAV with a hyperspectral camera (spectral range: 0.4–1.0 m; 150–300 channels; resolution: 2–4 nm). The mission integrated both ground spectrometry, airborne spectral imaging as well as automated classification to distinguish between the Cannabis and Ephedra and the surrounding vegetation. Findings The patterns of reflectance were distinguished. Cannabis could be differentiated with nettle, stubble and grass in the 500–820 nm range. There was spectral divergence by ephedra against shrubs and rocks notably in the range 750 nm. Digital mapping allowed visualising plant groupings according to their spectral signatures. Conclusions The new hyperspectral strategy, which involves use of UAV, is quick, less costly as well as accurate as compared to satellite monitoring. It enables quick investigation and monitoring of illegal cultivation of crops. The technology has a potential in expanding its usage into illegal mining, surveillance of crime against wildlife, and environmental monitoring.

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Publication Details

Journal
Discover Applied Sciences
Published
2025-10-14
DOI
https://doi.org/10.1007/s42452-025-07693-9
Primary Topic
Fire effects on ecosystems
Type
article
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article

UAV mounted spectrometry camera solution for detecting illicit cannabis and ephedra plantations

A. V. Seleznev, Dmitry Mikhaylov, Devadas R. Thangavelloo, Natalia Zolotykh
Discover Applied Sciences
Fire effects on ecosystems
article

UAV mounted spectrometry camera solution for detecting illicit cannabis and ephedra plantations

A. V. Seleznev, Dmitry Mikhaylov, Devadas R. Thangavelloo, Natalia Zolotykh
article en

Abstract

Abstract Background There is a noticeable threat to the law enforcement posed by the proliferation of the cultivation of drugs around the world. One of the most popular illegal cultivated plants are Cannabis sativa and Ephedra sinica. This paper presents a new way of detecting such crops with hyperspectral image application using unmanned aerial vehicles (UAVs) to find such crops in inaccessible mountainous lands. Techniques It was a field study within the frames of UNODC mission in Kyrgyzstan by using a fixed-wing UAV with a hyperspectral camera (spectral range: 0.4–1.0 m; 150–300 channels; resolution: 2–4 nm). The mission integrated both ground spectrometry, airborne spectral imaging as well as automated classification to distinguish between the Cannabis and Ephedra and the surrounding vegetation. Findings The patterns of reflectance were distinguished. Cannabis could be differentiated with nettle, stubble and grass in the 500–820 nm range. There was spectral divergence by ephedra against shrubs and rocks notably in the range 750 nm. Digital mapping allowed visualising plant groupings according to their spectral signatures. Conclusions The new hyperspectral strategy, which involves use of UAV, is quick, less costly as well as accurate as compared to satellite monitoring. It enables quick investigation and monitoring of illegal cultivation of crops. The technology has a potential in expanding its usage into illegal mining, surveillance of crime against wildlife, and environmental monitoring.

Discover Applied SciencesVol. 7(10)
National University of Singapore (SG), Kyrgyz State Technical University (KG), Kyrgyz National University (KG), University of Wales (GB)
Openalex Percentile: Top 73%
Fire effects on ecosystems
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UAV mounted spectrometry camera solution for detecting illicit cannabis and ephedra plantations — A. V. Seleznev, Dmitry Mikhaylov, et al. · Discover Applied Sciences (2025) | TGRS Research Map | TGRS