| 光谱学与光谱分析 |
|
|
|
|
|
| Research on Spectra Recognition Method for Cabbages and Weeds Based on PCA and SIMCA |
| ZU Qin1, 2, 3, DENG Wei1, 2*, WANG Xiu1, 2, ZHAO Chun-jiang1, 2 |
1. Beijing Research Center of Intelligent Equipment for Agriculture, Beijing Academy of Agriculture and Forestry Sciences，Beijing 100097，China
2. National Research Center of Intelligent Equipment for Agriculture，Beijing 100097，China
3. The Electrical Engineering College of Guizhou University，Guiyang 550025，China |
|
|
|
|
Abstract In order to improve the accuracy and efficiency of weed identification, the difference of spectral reflectance was employed to distinguish between crops and weeds. Firstly, the different combinations of Savitzky-Golay (SG) convolutional derivation and multiplicative scattering correction (MSC) method were applied to preprocess the raw spectral data. Then the clustering analysis of various types of plants was completed by using principal component analysis (PCA) method, and the feature wavelengths which were sensitive for classifying various types of plants were extracted according to the corresponding loading plots of the optimal principal components in PCA results. Finally, setting the feature wavelengths as the input variables, the soft independent modeling of class analogy (SIMCA) classification method was used to identify the various types of plants. The experimental results of classifying cabbages and weeds showed that on the basis of the optimal pretreatment by a synthetic application of MSC and SG convolutional derivation with SG’s parameters set as 1rd order derivation, 3th degree polynomial and 51 smoothing points, 23 feature wavelengths were extracted in accordance with the top three principal components in PCA results. When SIMCA method was used for classification while the previously selected 23 feature wavelengths were set as the input variables, the classification rates of the modeling set and the prediction set were respectively up to 98.6% and 100%.
|
|
Received: 2013-01-30
Accepted: 2013-04-20
|
|
|
|
Corresponding Authors:
ZU Qin
E-mail: dengw@nercita.org.cn
|
|
[1] TANG Jing-lei(唐晶磊). Yangling：Northwest A & F University(杨凌：西北农林科技大学)，2010.
[2] CHEN De-run，WANG Xiu，WANG Shu-mao(陈德润，王 秀，王书茂). Chinese Agricultural Mechanization(中国农机化)，2005，2：35.
[3] Thompson J F，Stafford J V，Miller P C H. Crop Protection，1991，10(4)：254.
[4] Karimi Y，Prasher S O，Patel R M，et al. Computers and Electronics in Agriculture，2006，51(2)：99.
[5] Wang Ning，Zhang Naiqian，Peterson D，et al. Transaction of the ASAE，2001，44(2)：409.
[6] Victor Alchanatis，Leonid Ridel，Amots Hetzroni，et al. Computers and Electronics in Agriculture，2005，47(3)：243.
[7] XIE Jun，PAN Tao，CHEN Jie-mei，et al(谢 军，潘 涛，陈洁梅, 等). Chinese Journal of Analytical Chemistry(分析化学)，2010，38(3)：342.
[8] CHEN Hua-zhou，PAN Tao，CHEN Jie-mei(陈华舟，潘 涛，陈洁梅). Computers and Applied Chemistry(计算机与应用化学)，2011，28(5)：518.
[9] Chu Xiaoli. Molecular Spectroscopy Analytical Technology Combined with Chemometrics and Its Applications(化学计量学方法与分子光谱分析技术). Beijing：Chemical Industry Press(北京：化学工业出版社)，2011. 45..
|
| [1] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
| [2] |
LI Quan-lun1, CHEN Zheng-guang1*, SUN Xian-da2. Rapid Detection of Total Organic Carbon in Oil Shale Based on Near
Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1691-1697. |
| [3] |
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3. Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1716-1720. |
| [4] |
MIAO Shu-guang1, SHAO Dan1*, LIU Zhong-yu2, 3, FAN Qiang1, LI Su-wen1, DING En-jie2, 3. Study on Coal-Rock Identification Method Based on Terahertz
Time-Domain Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1755-1760. |
| [5] |
TIAN Xue1, CHE Qian1, YAN Wei-min1, OU Quan-hong1, SHI You-ming2, LIU Gang1*. Discrimination of Millet Varieties and Producing Areas Based on Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1841-1847. |
| [6] |
MA Fang1, HUANG An-min2, ZHANG Qiu-hui1*. Discrimination of Four Black Heartwoods Using FTIR Spectroscopy and
Clustering Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1915-1921. |
| [7] |
WANG Ling-ling1, 2, 3, WANG Bo1, 2, 3, XIONG Feng1, 2, 3, YANG Lu-cun1, 2, LI Jing-jing4, XIAO Yuan-ming1, 2, 3, ZHOU Guo-ying1, 2*. A Comparative Study of Inorganic Elements in Cultivativing Astragalus Membranaceus From Different Habitats[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1407-1412. |
| [8] |
HOU Bing-ru1, LIU Peng-hui1, ZHANG Yang1, HU Yao-hua1, 2, 3*. Prediction of the Degree of Late Blight Disease Based on Optical Fiber Spectral Information of Potato Leaves[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1426-1432. |
| [9] |
TAN Yang1, WU Xiao-hong2, 3*, WU Bin4, SHEN Yan-jun1, LIU Jin-mao1. Qualitative Analysis of Pesticide Residues on Chinese Cabbage Based on GK Improved Possibilistic C-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1465-1470. |
| [10] |
YANG Yu-qing1, CAI Jiang-hui1, 2*, YANG Hai-feng1*, ZHAO Xu-jun1, YIN Xiao-na1. LAMOST Unknown Spectral Analysis Based on Influence Space and Data Field[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1186-1191. |
| [11] |
ZHANG Tian-liang, ZHANG Dong-xing, CUI Tao, YANG Li*, XIE Chun-ji, DU Zhao-hui, ZHONG Xiang-jun. Identification of Early Lodging Resistance of Maize by Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1229-1234. |
| [12] |
YAO Shan1, ZHANG Xuan-ling1, CAI Yu-xin1, HE Lian-qiong1, LI Jia-tong1, WANG Xiao-long1, LIU Ying1, 2*. Study on Distribution Characteristics of Different Nitrogen and
Phosphorus Fractions by Spectrophotometry in Baiyangdian
Lake and Source Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1306-1312. |
| [13] |
AIBIBAIHAN Maturzi1, XU Rong2, LI Xiao-jin1, 3*, FAN Cong-zhao3, QI Zhi-yong3, ZHU Jun3, WANG Guo-ping3, ZHAO Ya-qin3. Study on Infrared Spectrum Fingerprint of Apocynumvenetum L. in Xinjiang Based on Double Index Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 757-763. |
| [14] |
YANG En1, WANG Shi-bo2*. Study on Directional Near-Infrared Reflectance Spectra of Typical Types of Coal[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 847-858. |
| [15] |
DENG Shi-yu1, 2, LIU Cheng-zhi1, 4*, TAN Yong3*, LIU De-long1, ZHANG Nan1, KANG Zhe1, LI Zhen-wei1, FAN Cun-bo1, 4, JIANG Chun-xu3, LÜ Zhong3. A Combination of Multiple Deep Learning Methods Applied to Small-Sample Space Objects Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 609-615. |
|
|
|
|
