Hyperspectral Imaging With Machine Learning to Differentiate Cultivars, Growth Stages, Flowers, and Leaves of Industrial Hemp (Cannabis sativa L.)

Abstract

As an emerging cash crop, industrial hemp (Cannabis sativa L.) grown for cannabidiol (CBD) has spurred a surge of interest in the United States. Cultivar selection and harvest timing are important to produce CBD hemp profitably and avoid economic loss resulting from the tetrahydrocannabinol (THC) concentration in the crop exceeding regulatory limits. Hence there is a need for differentiating CBD hemp cultivars and growth stages to aid in cultivar and genotype selection and optimization of harvest timing. Current methods that rely on visual assessment of plant phenotypes and chemical procedures are limited because of its subjective and destructive nature. In this study, hyperspectral imaging was proposed as a novel, objective, and non-destructive method for differentiating hemp cultivars, growth stages as well as plant organs (leaves and flowers). Five cultivars of CBD hemp were grown greenhouse conditions and leaves and flowers were sampled at five growth stages 2–10 weeks in 2-week intervals after flower initiation and scanned by a benchtop hyperspectral imaging system in the spectral range of 400–1000 nm. The acquired images were subjected to image processing procedures to extract the spectra of hemp samples. The spectral profiles and scatter plots of principal component analysis of the spectral data revealed a certain degree of separation between hemp cultivars, growth stages, and plant organs. Machine learning based on regularized linear discriminant analysis achieved the accuracy of up to 99.6% in differentiating the five hemp cultivars. Plant organ and growth stage need to be factored into model development for hemp cultivar classification. The classification models achieved 100% accuracy in differentiating the five growth stages and two plant organs. This study demonstrates the effectiveness of hyperspectral imaging for differentiating cultivars, growth stages and plant organs of CBD hemp, which is a potentially useful tool for growers and breeders of CBD hemp.

Non‐invasive assessment of cultivar and sex of Cannabis sativa L. by means of hyperspectral measurement

“…We assume that this high prediction accuracy mainly relates to distinct chemotypes of the cultivars in our study, which have been selected based on commercial relevance for hemp seed production and from different vendors (Table 1). As the geographical and ecological range of cannabis is unusually broad, substantial differences in the genotypes and thus in the chemotypes can be expected, as reported earlier by Lynch et al (2016) andBorroto Fernandez et al (2020) for European hemp varieties. This variability evidently generates a high demand for innovative technologies to cheaply, quickly, and non-invasively determine cultivar identity in the growing cannabis market, for which the approach presented here proves to be ideally suited.…”

“…This variability evidently generates a high demand for innovative technologies to cheaply, quickly, and non‐invasively determine cultivar identity in the growing cannabis market, for which the approach presented here proves to be ideally suited. However, despite the increasing scientific evidence for the applicability of spectroscopic approaches to discriminate plant varieties, such as shown for tea (Li & He, 2008), tomato (Xu et al, 2009), eucalyptus (Kumar et al, 2010), tobacco (Seiffert et al, 2010), grapevine (Diago et al, 2013), and hemp (Lu et al, 2021) their industrial applications are still missing (Dos Santos et al, 2013; Lopes & Sousa, 2018). Recently, an automated system combining hyperspectral imaging and machine learning for the classification of grapevine varieties under field conditions has been described (Gutiérrez et al, 2018).…”

“…This diversity is likely related to two linked gene clusters of functional and non-functional genes, which encode different versions of the two main enzymes involved in either Δ9-THC or CBD synthesis. In addition, these genes are interspersed with a multitude of mobile genetic elements, making sequencing more challenging and genetic rearrangements in other, not yet sequenced, cultivars more likely (Grassa et al, 2021;Lynch et al, 2016;Schilling et al, 2020). High Δ9-THC levels characterize medicinal hemp while industrial hemp is defined by high CBD and low Δ9-THC (<0.1%-1%) levels, depending on the country and state (Duppong, 2009;Schluttenhofer & Yuan, 2017).…”

Cotton aphid infestation monitoring using Sentinel-2 MSI imagery coupled with derivative of ratio spectroscopy and random forest algorithm

“…In addition to feature extraction, the factors that affect the accuracy of pest classification are also related to the selection of classification methods. At present, machine learning algorithms have been applied in many fields and achieved good results ( Lu et al., 2022 ; Maia et al., 2022 ). In this study, five machine learning algorithms were selected for comparison of classification accuracy, and the best classification performance was achieved by RF, demonstrating the good performance of the algorithm in terms of pest classification potential.…”

A Survey of Modern Greenhouse Technologies and Practices for Commercial Cannabis Cultivation

“…1) It was published between 01/01/2018 and 06/05/2022 (dd/mm/yyyy) 2) Document type is an article 3) The publication stage is finale 4) The publication is written in English When restricted to cannabis, the initial search term returned ten results, of which none were deemed relevant, comprising of one paper that used hyperspectral imagery to determine the stage of growth [89], two papers investigated identifying cannabis from aerial/satellite images [90], [91], three investigated improving in-vitro germination/seedling growth [92], [93], [94], and four looked at either its health effect or identification of psychoactive metabolites [5], [95], [96], [97]. For this reason, the search term was broadened to include 'greenhouse' and 'grow room', allowing for articles on any crop that discussed modern technological approaches for improving the greenhouses.…”