Multispectral reflectance dataset of medicinal Cannabis sativa cultivars for varietal classification

The dataset comprises 800 unique spectral signatures collected from four distinct cultivars: Embera CBD, Yocoto CBD, Limón CBD, and Valle de los Umbras. Data acquisition was performed using an Ocean Optics RED TIDE USB650 spectroradiometer with an optical fiber probe, covering a wavelength range from 410 nm to 890 nm. Measurements were taken under controlled ambient light conditions within a greenhouse, with cultivation parameters standardized. Spectral signatures were obtained from 30 plants of Embera CBD and Valle de los Umbras, and 20 plants of Limón CBD and Yocoto CBD varieties. Data collection spanned three distinct phenological stages (early vegetative, early flowering, and mid-flowering) and included measurements from both upper and lower leaves. Each signature represents an average of three measurements per leaf sample, calibrated with a white reflectance reference panel. Raw data were processed and normalized using Spectroscopy OceanView software, R, and JASP. The dataset is structured with columns for date (phenological stage), variety, plant ID, leaf location, and 2442 reflectance values corresponding to specific wavelengths. This publicly available dataset serves as a valuable resource for developing and training machine learning models for non-destructive and cost-effective classification of <i>Cannabis</i> varieties based on spectral characteristics, and for exploring correlations between spectral signatures and plant properties like cannabinoid content.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2025-10-17
DOI
https://doi.org/10.1016/j.dib.2025.112176
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multispectral reflectance dataset of medicinal Cannabis sativa cultivars for varietal classification

Vladimir Henao-Céspedes, Yeison Alberto Garcés-Gómez, Oscar Cardona-Morales, Jolián Andrés Vargas-Alzate et al.
Data in Brief
Remote Sensing in Agriculture
article

Multispectral reflectance dataset of medicinal Cannabis sativa cultivars for varietal classification

Vladimir Henao-Céspedes, Yeison Alberto Garcés-Gómez, Oscar Cardona-Morales, Jolián Andrés Vargas-Alzate, Julián Ricardo León-Zuleta
article en

Abstract

The dataset comprises 800 unique spectral signatures collected from four distinct cultivars: Embera CBD, Yocoto CBD, Limón CBD, and Valle de los Umbras. Data acquisition was performed using an Ocean Optics RED TIDE USB650 spectroradiometer with an optical fiber probe, covering a wavelength range from 410 nm to 890 nm. Measurements were taken under controlled ambient light conditions within a greenhouse, with cultivation parameters standardized. Spectral signatures were obtained from 30 plants of Embera CBD and Valle de los Umbras, and 20 plants of Limón CBD and Yocoto CBD varieties. Data collection spanned three distinct phenological stages (early vegetative, early flowering, and mid-flowering) and included measurements from both upper and lower leaves. Each signature represents an average of three measurements per leaf sample, calibrated with a white reflectance reference panel. Raw data were processed and normalized using Spectroscopy OceanView software, R, and JASP. The dataset is structured with columns for date (phenological stage), variety, plant ID, leaf location, and 2442 reflectance values corresponding to specific wavelengths. This publicly available dataset serves as a valuable resource for developing and training machine learning models for non-destructive and cost-effective classification of <i>Cannabis</i> varieties based on spectral characteristics, and for exploring correlations between spectral signatures and plant properties like cannabinoid content.

Data in BriefVol. 63
Universidad Autonoma de Manizales (CO), Universidad de Manizales (CO), Universidad Católica de Manizales (CO)
Openalex Percentile: Top 67%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.