Detection and identification of Cannabis sativa L. using near infrared hyperspectral imaging and machine learning methods. A feasibility study

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 237; p. 118385
• Main Authors: Pereira, José Francielson Q., Pimentel, Maria Fernanda, Amigo, José Manuel, Honorato, Ricardo S.
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15-08-2020
• Subjects: Brazil; Cannabis - chemistry; Cannabis sativa Linnaeu; Cheminformatics; Feasibility Studies; Hyperspectral image; Hyperspectral Imaging - methods; Hyperspectral Imaging - statistics & numerical data; Machine Learning; Near infrared; Plant Leaves - chemistry; Principal Component Analysis; Reproducibility of Results; Sensitivity and Specificity; Soft Independent Class Analogy model; Sparse-Principal Component Analysis; Spectroscopy, Near-Infrared - methods; Brazil; Cannabis sativa Linnaeu; Hyperspectral image; Soft Independent Class Analogy model; Near infrared; Sparse-Principal Component Analysis
• DOI: 10.1016/j.saa.2020.118385
• EISSN: 1873-3557
• ISSN: 1386-1425

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.
[Display omitted]
•Identification of Cannabis sativa L using near infrared hyperspectral images•Sparse Principal Component Analysis to select 4 essential wavelengths for Cannabis s•Sparse selection and Soft Independent Class Analogy model for Cannabis identification•Sensitivity and specificity values of 89.45% and 97.60% of sparse SIMCA•Effective identification of Cannabis surrounded by other plants and soil