The invention discloses a
tobacco leaf grading method based on a hyperspectral image and a
deep learning algorithm. The
tobacco leaf grading method comprises steps of 1, obtaining hyperspectral image data of a
tobacco leaf sample to be measured, 2, performing high level characteristic extraction on the image data to perform dimension reduction, and 3, performing classification on obtained image information and spectral information. A hardware platform of a
hyperspectral imaging system comprises a
light source, a light splitting module, an area array
CCD detector and a computer provided with an image collection card; spectral information can be obtained while the imaging
system is utilized to perform image information collection, separate collection is not needed and
collection time is shortened; in the step 2, a
convolutional neural network is utilized to perform pre-
processing and then a
deep belief network is utilized to perform characteristic extraction; in the step 3, a Sofmax layer is added on the top layer and obtained characteristics are inputted into a softmax regression classifier to realize classification. The tobacco leaf grading method based on the hyperspectral image and
deep learning can maximally achieve lossless grading, accurately divides a tobacco leaf grade, and ensures benefits of a
purchasing party.