高光谱无损识别野生和种植黑枸杞

doi:10.3964/j.issn.1000-0593(2021)01-0201-05

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Abstract Hyperspectral image technology has a broad application in the detection and identification of agricultural products. Wild black Goji berries have remarkable economic benefits, and are often impersonated by growing black Goji berries. A nondestructive and fast identification method for wild black Goji berries using hyperspectral image technology is proposed. Obtained results were as follows:(1) a total of 256 samples of black Goji berries (Wild，Growing, 128 each) in the range of 900～1 700 nm were observed, and each average spectra were used as simple spectra. (2) spectral is preprocessed with standardized normal variate transform (SNV) based on the Kennard-Stone(K-S) method, the calibration set and prediction set samples ratio were observed in 2∶1 (pairs). However, the spectra were found reduced in dimension by the successive projections algorithm method (SPA), and the 30 characteristic wavelengths extracted by the full spectra (FS). Then the 30 characteristic wavelengths and the full spectra are used as model inputs, the support vector machine (SVM), extreme learning machine (ELM), and random forest (RF) recognition models were established. (3) In the identification of wild black Goji berries models, the results showed that the calibration identification rate of SVM, ELM, and RF model with reference to FS and SPA were higher than 98.8%, and the prediction set samples rate of SVM, ELM, and RF model were also higher than 97.7%. The identification model of FS was slightly better than the identification model of SPA. However, the characteristic wave constant extracted by SPA is 11.8% less compared to FS, which eventually reduces the calculated model. RF identification model was reported better compared to SVM, and ELM, RF identification rate is 100%. The study has shown that the use of hyperspectral image technology combined with classification models can quickly identify wild black Goji berries.

Key words：Wild black Goji berry; Hyperspectral image technology; Support vector machine; Extreme learning machine; Random forest

Received: 2019-11-29 Accepted: 2020-03-22

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)01-0201-05 OR https://www.gpxygpfx.com/EN/Y2021/V41/I01/201