• Spectroscopy and Spectral Analysis
  • Vol. 42, Issue 4, 1076 (2022)
Rui DONG, Zhuang-sheng TANG, Rui HUA, Xin-cheng CAI, Dar-han BAO, Bin CHU, Yuan-yuan HAO, and Li-min HUA*;
Author Affiliations
  • Grassland College of Gansu Agricultural University, Key Laboratory of Grassland Ecosystem Ministry of Education, Engineering and Technology Research Center for Alpine Rodent Pest Control, National Forestry and Grassland Administration, Lanzhou 730070, China
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    DOI: 10.3964/j.issn.1000-0593(2022)04-1076-07 Cite this Article
    Rui DONG, Zhuang-sheng TANG, Rui HUA, Xin-cheng CAI, Dar-han BAO, Bin CHU, Yuan-yuan HAO, Li-min HUA. Research on Classification Method of Main Poisonous Plants in Alpine Meadow Based on Spectral Characteristic Variables[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1076 Copy Citation Text show less
    Average spectral curves of 11 different alpine meadow poisonous plants
    Fig. 1. Average spectral curves of 11 different alpine meadow poisonous plants
    The average first-order differential spectrum curve of 11 different alpine meadow poisonous plants
    Fig. 2. The average first-order differential spectrum curve of 11 different alpine meadow poisonous plants
    Kendall correlation matrixNote: The graph is divided into two parts, the upper triangle is the significance test, the lower triangle is the correlation coefficient, the asterisk in the figure represents the significance test, * means the difference is significant, no * means the difference is not significant, *p≤ 0.01
    Fig. 3. Kendall correlation matrix
    Note: The graph is divided into two parts, the upper triangle is the significance test, the lower triangle is the correlation coefficient, the asterisk in the figure represents the significance test, * means the difference is significant, no * means the difference is not significant, *p≤ 0.01
    Sorting of 16 spectral feature variables(a): The cumulative percentage of typical variables; (b): The standardized score coefficient
    Fig. 4. Sorting of 16 spectral feature variables
    (a): The cumulative percentage of typical variables; (b): The standardized score coefficient
    Classification accuracy of 16 spectral variables in 5 algorithms
    Fig. 5. Classification accuracy of 16 spectral variables in 5 algorithms
    毒草名毒草化学成分部位类型代表
    黄花棘豆(Oxytropis ochrocephala)吲哚兹定生物碱全株有毒OO
    宽苞棘豆(O latibracteata)吲哚兹定生物碱全株有毒OL
    多枝黄芪(Astragalus polycladus)吲哚兹定生物碱全株有毒AP
    长毛风毛菊(Saussurea hieracioides)倍半萜全草有毒SH
    黄帚橐吾(Ligularia virgaurea)倍半萜全草有毒LV
    乳白香青(Anaphalis lactea)倍半萜全草有毒AL
    葵花大蓟(Cirsium souliei)倍半萜全草有毒CS
    瑞香狼毒(Stellera chamaejasme)黄酮类、 萜类、 本质素类全株有毒SC
    密花香薷(Elsholtzia densa)黄酮类化合物全草有毒ED
    露蕊乌头(Aconitum gymnandrum)生物碱全草有毒, 块根剧毒AG
    碎米蕨叶马先蒿(Pedicularis cheilanrthifolia)甾体或三萜类、 生物碱、 黄酮、 强心苷全草有毒PC
    Table 1. 11 poisonous plants and their characteristics
    参数类型参数符号定义
    位置变量红边幅值Mre在红边680~760 nm内一阶微分最大值
    红边位置Lre红边幅值对应波长
    红谷幅值Mr红光范围内640~680 nm最大反射率
    红谷位置Lr红光范围内640~680 nm红谷对应波长
    绿峰幅值Mg绿光范围内510~560 nm最大反射率
    绿峰位置Lg绿光范围内510~560 nm绿峰对应的波长
    蓝边幅值Mb在蓝边490~530 nm内一阶微分最大值
    蓝边位置Lb蓝边幅值对应的波长
    黄边幅值My在黄边560~640 nm内一阶微分最大值
    黄边位置Ly黄边幅值对应的波长
    面积变量红边面积Are红边范围内一阶微分值总和
    蓝边面积Ab蓝边范围内一阶微分值总和
    植被指数变量Mg/MrRVI1绿峰与红谷幅值比值
    Are/AbRVI2红边与蓝边面积比值
    (Mg-Mr)/(Mg+Mr)NDVI1绿峰与红谷幅值归一化比值
    (Are-Ab)/(Are+Ab)NDVI2红边与蓝边面积归一化比值
    Table 2. Definition of spectral characteristic variables
    毒草种类APOOLVOLSCAGEDALPCSHCS样本生产者精度/%
    AP5050100
    OO3513115070
    LV438625076
    OL4465092
    SC5050100
    AG41455090
    ED21454893.75
    AL5050100
    PC4915098
    SH1495098
    CS1495098
    分类总和5639396150475150515149
    用户精度/%89.2889.7497.4375.4110095.7488.2410096.0896.08100
    Table 3. Classification accuracy based on typical discriminant analysis
    Rui DONG, Zhuang-sheng TANG, Rui HUA, Xin-cheng CAI, Dar-han BAO, Bin CHU, Yuan-yuan HAO, Li-min HUA. Research on Classification Method of Main Poisonous Plants in Alpine Meadow Based on Spectral Characteristic Variables[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1076
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