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Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study
- Source :
- Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy. 237
- Publication Year :
- 2020
-
Abstract
- Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.
- Subjects :
- 02 engineering and technology
010402 general chemistry
Machine learning
computer.software_genre
Cannabis sativa
01 natural sciences
Sensitivity and Specificity
Analytical Chemistry
Machine Learning
Instrumentation
Near infrared hyperspectral imaging
Spectroscopy
Cannabis
Principal Component Analysis
Spectroscopy, Near-Infrared
Chemistry
business.industry
Cheminformatics
External validation
Hyperspectral imaging
Reproducibility of Results
Spectral bands
Hyperspectral Imaging
021001 nanoscience & nanotechnology
Atomic and Molecular Physics, and Optics
0104 chemical sciences
Plant Leaves
Identification (information)
Principal component analysis
Feasibility Studies
Nir spectra
Artificial intelligence
0210 nano-technology
business
computer
Brazil
Subjects
Details
- ISSN :
- 18733557
- Volume :
- 237
- Database :
- OpenAIRE
- Journal :
- Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
- Accession number :
- edsair.doi.dedup.....3c6964b1fe6467422a877a142e07dfa4