Detection Method of Green Potato Based on Hyperspectral Imaging

Abstract:

To solve the problems of difficulties in detecting the slightly green potatoes placed randomly, two detection methods were compared based on the semitransmission and reflection hyperspectral imaging technologies and then a more optimal detection method was determined. 225 potatoes samples were selected, including 122 normal samples and 103 green samples. Semitransmission and reflection hyperspectral imaging technologies were used to extract the RGB, HSV and Lab color information from the image; the isometric mapping (Isomap), the maximum variance unfolding (MVU) and the Laplacian feature mapping (LE) were utilized to reduce the dimension of image information. Semitransmission and reflection hyperspectral imaging technologies were used to extract the average spectrum from the spectral region of interest; the linearity preserving projection (LPP), the local tangent space alignment (LTSA) and the locally linear coordination(LLC) were utilized to reduce the dimension of spectral information. The deep belief networks (DBN) model which is a kind of deep learning approach was developed based on the image and spectrums of different hyperspectral imaging ways. The multisource information fusion technology was used to optimize the model with a high detection accuracy and different detection models were built based on different ways of imaging or the fusion of image and spectrum. The results show that the fusion model, which is developed based on the semitransmission hyperspectral imaging and the reflection hyperspectral imaging, is the best option. Its detection rate can reach 100% in both the calibration and the validation. Nondistractive detecting of the slightly green potatoes can be realized with this fusion model.