Xinjiang Agricultural Sciences ›› 2023, Vol. 60 ›› Issue (3): 616-623.DOI: 10.6048/j.issn.1001-4330.2023.03.012
• Horticultural Special Local Products·Physiology and Biochemistry • Previous Articles Next Articles
GUO Yang1(), GUO Junxian1(), SHI Yong1, LIU Li2, FANG Wenyan2, LIU Yancen1
Received:
2022-07-08
Online:
2023-03-20
Published:
2023-04-18
Correspondence author:
GUO Junxian(1975-), male, from Balikun County, Xinjiang, Professor, research direction is nondestructive testing of agricultural products, (E-mail)Supported by:
郭阳1(), 郭俊先1(), 史勇1, 刘丽2, 方文艳2, 刘彦岑1
通讯作者:
郭俊先(1975-),男,新疆巴里坤人,教授,博士,硕士生/博士生导师,研究方向为农产品无损检测,(E-mail)junxianguo@163.com
作者简介:
郭阳(1995-),男,辽宁沈阳人,硕士研究生,研究方向为农产品无损检测,(E-mail)2744103108@qq.com
基金资助:
CLC Number:
GUO Yang, GUO Junxian, SHI Yong, LIU Li, FANG Wenyan, LIU Yancen. Prediction of SPAD Value in Melon Leaves by Characteristic Wavelength Screening Combined with PCA-LSSVM[J]. Xinjiang Agricultural Sciences, 2023, 60(3): 616-623.
郭阳, 郭俊先, 史勇, 刘丽, 方文艳, 刘彦岑. 特征波长筛选结合PCA-LSSVM对甜瓜叶片SPAD值的预测[J]. 新疆农业科学, 2023, 60(3): 616-623.
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样本集 Sample set | 样本数 Sample size | SPAD值 | ||
---|---|---|---|---|
平均值 Mean value | 最大值 Maximum value | 最小值 Minimum value | ||
校正集 Calibration set | 75 | 53.0 | 61.9 | 43.1 |
预测集 Prediction set | 25 | 52.5 | 61.5 | 45.1 |
Tab.1 SPAD value of relative content of chlorophyll in Melon
样本集 Sample set | 样本数 Sample size | SPAD值 | ||
---|---|---|---|---|
平均值 Mean value | 最大值 Maximum value | 最小值 Minimum value | ||
校正集 Calibration set | 75 | 53.0 | 61.9 | 43.1 |
预测集 Prediction set | 25 | 52.5 | 61.5 | 45.1 |
不同光谱预处理 Different Spectral pretreatment | PC | RC | RMSEC | RP | RMSEP | RPD |
---|---|---|---|---|---|---|
Origina | 8 | 0.821 5 | 1.281 6 | 0.563 5 | 1.477 8 | 1.552 1 |
Autoscales | 5 | 0.762 3 | 1.384 4 | 0.740 5 | 1.354 4 | 1.974 1 |
SNVT | 5 | 0.762 3 | 1.384 4 | 0.740 5 | 1.354 4 | 1.974 1 |
SavitZky-Golay | 8 | 0.532 9 | 2.875 7 | 0.601 2 | 2.517 7 | 1.583 7 |
1st deriative | 3 | 0.789 4 | 1.330 9 | 0.766 6 | 1.264 5 | 2.106 3 |
MA | 8 | 0.821 2 | 1.287 8 | 0.563 9 | 1.477 2 | 1.552 8 |
Normalize | 12 | 0.879 6 | 1.011 1 | 0.350 6 | 2.024 5 | 1.243 2 |
Tab.2 PLS model of chlorophyll content with different pretreatment methods
不同光谱预处理 Different Spectral pretreatment | PC | RC | RMSEC | RP | RMSEP | RPD |
---|---|---|---|---|---|---|
Origina | 8 | 0.821 5 | 1.281 6 | 0.563 5 | 1.477 8 | 1.552 1 |
Autoscales | 5 | 0.762 3 | 1.384 4 | 0.740 5 | 1.354 4 | 1.974 1 |
SNVT | 5 | 0.762 3 | 1.384 4 | 0.740 5 | 1.354 4 | 1.974 1 |
SavitZky-Golay | 8 | 0.532 9 | 2.875 7 | 0.601 2 | 2.517 7 | 1.583 7 |
1st deriative | 3 | 0.789 4 | 1.330 9 | 0.766 6 | 1.264 5 | 2.106 3 |
MA | 8 | 0.821 2 | 1.287 8 | 0.563 9 | 1.477 2 | 1.552 8 |
Normalize | 12 | 0.879 6 | 1.011 1 | 0.350 6 | 2.024 5 | 1.243 2 |
处理方法 Processing method | 校正集 Calibration set | 预测集 Prediction set | ||
---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | |
MC-UVE | 0.831 4 | 1.441 9 | 0.739 1 | 2.031 7 |
GA | 0.888 1 | 0.990 4 | 0.720 6 | 1.843 7 |
CARS | 0.881 8 | 0.976 1 | 0.804 5 | 1.810 4 |
MC-UVE+PCA | 0.863 3 | 1.149 0 | 0.749 8 | 1.830 2 |
GA+PCA | 0.867 4 | 1.101 4 | 0.859 0 | 1.129 3 |
CARS+PCA | 0.875 6 | 1.032 8 | 0.872 6 | 1.252 6 |
Tab.3 Modeling and prediction of spectral variables combined with ELM
处理方法 Processing method | 校正集 Calibration set | 预测集 Prediction set | ||
---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | |
MC-UVE | 0.831 4 | 1.441 9 | 0.739 1 | 2.031 7 |
GA | 0.888 1 | 0.990 4 | 0.720 6 | 1.843 7 |
CARS | 0.881 8 | 0.976 1 | 0.804 5 | 1.810 4 |
MC-UVE+PCA | 0.863 3 | 1.149 0 | 0.749 8 | 1.830 2 |
GA+PCA | 0.867 4 | 1.101 4 | 0.859 0 | 1.129 3 |
CARS+PCA | 0.875 6 | 1.032 8 | 0.872 6 | 1.252 6 |
处理方法 Processing method | 校正集 Calibration set | 预测集 Prediction set | ||
---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | |
MC-UVE | 0.953 7 | 0.003 4 | 0.812 9 | 0.010 8 |
GA | 0.576 3 | 0.027 4 | 0.526 2 | 0.032 8 |
CARS | 0.903 5 | 0.006 8 | 0.893 1 | 0.008 6 |
MC-UVE+PCA | 0.877 3 | 0.009 2 | 0.793 8 | 0.011 8 |
GA+PCA | 0.897 0 | 0.007 2 | 0.883 0 | 0.008 3 |
CARS+PCA | 0.878 1 | 0.008 5 | 0.843 1 | 0.010 5 |
Tab.4 Modeling and prediction effect of spectral variable processing combined with SVM
处理方法 Processing method | 校正集 Calibration set | 预测集 Prediction set | ||
---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | |
MC-UVE | 0.953 7 | 0.003 4 | 0.812 9 | 0.010 8 |
GA | 0.576 3 | 0.027 4 | 0.526 2 | 0.032 8 |
CARS | 0.903 5 | 0.006 8 | 0.893 1 | 0.008 6 |
MC-UVE+PCA | 0.877 3 | 0.009 2 | 0.793 8 | 0.011 8 |
GA+PCA | 0.897 0 | 0.007 2 | 0.883 0 | 0.008 3 |
CARS+PCA | 0.878 1 | 0.008 5 | 0.843 1 | 0.010 5 |
处理方法 Processing method | 校正集 Calibration set | 预测集 Prediction set | ||
---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | |
MC-UVE | 0.990 0 | 0.000 1 | 0.792 3 | 0.012 4 |
GA | 0.674 3 | 0.021 7 | 0.633 1 | 0.023 6 |
CARS | 0.890 0 | 0.007 1 | 0.873 1 | 0.008 7 |
MC-UVE+PCA | 0.993 1 | 0.000 7 | 0.744 7 | 0.014 3 |
GA+PCA | 0.955 8 | 0.004 3 | 0.939 7 | 0.005 1 |
CARS+PCA | 0.986 6 | 0.001 1 | 0.776 9 | 0.017 7 |
Tab.5 Modeling and prediction effect of spectral variable processing combined with LSSVM
处理方法 Processing method | 校正集 Calibration set | 预测集 Prediction set | ||
---|---|---|---|---|
Rc | RMSEC | Rp | RMSEP | |
MC-UVE | 0.990 0 | 0.000 1 | 0.792 3 | 0.012 4 |
GA | 0.674 3 | 0.021 7 | 0.633 1 | 0.023 6 |
CARS | 0.890 0 | 0.007 1 | 0.873 1 | 0.008 7 |
MC-UVE+PCA | 0.993 1 | 0.000 7 | 0.744 7 | 0.014 3 |
GA+PCA | 0.955 8 | 0.004 3 | 0.939 7 | 0.005 1 |
CARS+PCA | 0.986 6 | 0.001 1 | 0.776 9 | 0.017 7 |
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