Lu Yuzhen, Young Sierra, Linder Eric, Whipker Brian, Suchoff David
Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, United States.
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, United States.
Front Plant Sci. 2022 Feb 2;12:810113. doi: 10.3389/fpls.2021.810113. eCollection 2021.
As an emerging cash crop, industrial hemp ( 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.
作为一种新兴的经济作物，用于生产大麻二酚（CBD）的工业大麻在美国引发了人们极大的兴趣。品种选择和收获时机对于盈利性地生产CBD大麻以及避免因作物中四氢大麻酚（THC）浓度超过监管限制而导致经济损失至关重要。因此，需要区分CBD大麻品种和生长阶段，以辅助品种和基因型选择以及收获时机的优化。目前依赖于植物表型视觉评估和化学程序的方法存在局限性，因为其具有主观性和破坏性。在本研究中，提出了高光谱成像作为一种新颖、客观且无损的方法，用于区分大麻品种、生长阶段以及植物器官（叶子和花朵）。在温室条件下种植了五个CBD大麻品种，并在开花后2至10周的五个生长阶段，以两周为间隔对叶子和花朵进行采样，然后用台式高光谱成像系统在400 - 1000 nm光谱范围内进行扫描。对获取的图像进行图像处理程序，以提取大麻样品的光谱。光谱数据的主成分分析的光谱轮廓和散点图显示，大麻品种、生长阶段和植物器官之间存在一定程度的分离。基于正则化线性判别分析的机器学习在区分五个大麻品种时准确率高达99.6%。在大麻品种分类的模型开发中需要考虑植物器官和生长阶段。分类模型在区分五个生长阶段和两个植物器官时准确率达到了100%。本研究证明了高光谱成像在区分CBD大麻品种、生长阶段和植物器官方面的有效性，这对于CBD大麻种植者和育种者来说是一种潜在有用的工具。
