基于模型集群的马铃薯叶绿素检测光谱变量筛选讨论

doi:10.3964/j.issn.1000-0593(2020)07-2259-08

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Abstract The paper was aimed to explore the chlorophyll spectral absorption characteristics of potato crops, fully analyze the spectral characteristic wavelength variables, and establish a high--precision chlorophyll content detection model. The 314 reflectance samples were collected using an ASD portable spectrometer at the seedling stage (M1), tuber formation stage (M2), tuber expansion stage (M3) and starch accumulation stage (M4). The chlorophyll content was determined by the simultaneous collection of leaves. After spectral data pre--treatment, the spectral reflectance changes of different growth stages of potato were analyzed. The algorithms based on model population analysis were used to select chlorophyll characteristiccharacteristic chlorophyll wavelengths, including Monte Carlo uninformative variables elimination (MC--UVE), random frog (RF) and competitive adaptive reweighted sampling (CARS) algorithm. The partial least square regression (PLSR) was used to establish the chlorophyll content detection model. The sample set was divided by a ratio of 3∶1 in each growth stage using the sample set partitioning based on joint X-Y distance algorithm (SPXY) with the 240 calibration samples and 74 validation samples. The different algorithms (MC-UVE, RF, CARS) were used to select chlorophyll characteristic wavelengths. The influence of the number of iteration (N) and the number of the latent variables (LV) on the results of characteristic wavelength selection of MC-UVE and RF algorithms were discussed, and the influences of N on that of CARS algorithm were discussed. Six gradients were set for the number of iterations (N), which were N=50, 100, 500, 1 000, 5 000 and 10 000, respectively. Four gradients were set for the number of latent variables (LV), which were LV=15, 20, 25 and 30 respectively. Taking the validation set result of PLS model as the evaluation index, the optimal parameter combination of N and LV was analyzed. Based on the optimal characteristic wavelengths selected by the three algorithms, the chlorophyll detection PLSR models were established and denoted as RF-PLSR, MC-UVE-PLSR, and CARS-PLSR, respectively. The research results showed that the chlorophyll characteristic wavelengths selection results were optimal when N=50 and LV=30 of MC-UVE, N=500 and LV=30 of RF, N=100 of CARS. By comparing the RF-PLSR, MC-UVE-PLSR, and CARS-PLSR models, it was indicated that the performance of the RF-PLSR model was best, the determination coefficient of validation (R²_v) was 0.786, the root means square error of validation (RMSEV) was 3.415 mg·L^-1; MC-UVE-PLSR was second, the R²_v was 0.696, the RMSEV was 4.072; and the CARS-PLSR was the worst, the R²_v was 0.689, the RMSEV was 4.183. Above results showed that the RF algorithm was superior to MC-UVE and CARS in selecting the characteristic chlorophyll wavelength of potato.

Key words：Potato; Chlorophyll detection; Model population analysis; Band selection; Partial least square (PLS)

Received: 2019-06-13 Accepted: 2019-10-24

ZTFLH:

O433.4

Corresponding Authors: SUN Hong E-mail: sunhong@cau.edu.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)07-2259-08 OR https://www.gpxygpfx.com/EN/Y2020/V40/I07/2259