Abstract: Dendrobium nobile Lindl. ( D. nobile ), as an important traditional Chinese medicine and highly ornamental value plant, has attracted more and more people’s attention. In order to meet the needs of tracking and testing the growth status of D. nobile , the visible-near infrared hyperspectral imaging technology was proposed for nitrogen nutrients detection in vivo in its different growth stages. Firstly, collecting the hyperspectral images of D . nobile in spectral range 400-1000 nm. Secondly, extracting the region of interesting (ROI). The 2G-R-B algorithm was used to segment the background and plants, and then the RGB threshold method was used to separate the leaf sheath and stems. Removing noise by two masks’ or-operations, and then the ROI area was finally extracted by selecting the largest area. After that, the reflectance spectrum of the ROI area was extracted, and then two kinds of feature extraction methods and two kinds of optimizing band selection methods were researched for dimension reduction of hyperspectral images. Finally, Support vector machine (SVM) model was established to classify the nitrogen level of D. nobile . The results showed that the LDA combined with the SVM algorithm had the highest classification accuracy. The classification accuracy of training sets in the three growth stages were 97.47%, 95.03%, and 95.97%, respectively, and the classification accuracy of test set reached 97.00%, 88.8%, 92.67%. The visible-near infrared hyperspectral imaging technology combining LDA-SVM classification model could effectively distinguish D. nobile cultivated by gradient nitrogen in each growth stage. It is a potential technology applied in decision-making of precise nutrition supply. Keywords: facility gardening, hyperspectral imaging technology, Dendrobium nobile Lindl., nitrogen detection, SVM DOI:  10.33440/j.ijpaa.20200302.87  Citation: Long T, Long Y B, Liu H C, Liu H L, Wang Z H, Zhao J. Nitrogen detection of Dendrobium nobile based on hyperspectral images.  Int J Precis Agric Aviat, 2020; 3(2): 73–82.