Diagnosis the dust pollution stress of wheat leaves based on hyperspectral technology. Liang, L., Li, Y., Sun, Q., Chen, Q., Di, L., & Deng, M. In 2015 Fourth International Conference on Agro-Geoinformatics (Agro-geoinformatics), pages 192–195, July, 2015. doi abstract bibtex Wheat leaves under different dust stress intensity were diagnosed with hyperspctrectral technology. A four-level dust stress experiment was conducted using the wheat in growing season as the study object. The spectra of wheat leaves which suffered different stress intensity levels,including severe stress, moderate stress, mild stress and no stress, were collected by ASD FieldSpec®3 spectrometer, and 32 samples were used for each level. All of the samples were divided randomly into two groups, one group with 96 ones used as calibrated set, and another with 32 ones as validated set. The spectra data were then pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). Using the anterior 6 principal components computed by PCA as the model input variables, and the values of stress level as the output variables, the hyperspectral diagnosis models of dust stress intensity were established. And then the 32 unknown samples in the validated set were predicted by the diagnosis model. The result showed that the accuracy of model prediction was 87.5%, indicated it was feasible to diagnose the dust stress intensity with hyperspctral technology.
@inproceedings{liang_diagnosis_2015,
title = {Diagnosis the dust pollution stress of wheat leaves based on hyperspectral technology},
doi = {10.1109/Agro-Geoinformatics.2015.7248096},
abstract = {Wheat leaves under different dust stress intensity were diagnosed with hyperspctrectral technology. A four-level dust stress experiment was conducted using the wheat in growing season as the study object. The spectra of wheat leaves which suffered different stress intensity levels,including severe stress, moderate stress, mild stress and no stress, were collected by ASD FieldSpec®3 spectrometer, and 32 samples were used for each level. All of the samples were divided randomly into two groups, one group with 96 ones used as calibrated set, and another with 32 ones as validated set. The spectra data were then pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). Using the anterior 6 principal components computed by PCA as the model input variables, and the values of stress level as the output variables, the hyperspectral diagnosis models of dust stress intensity were established. And then the 32 unknown samples in the validated set were predicted by the diagnosis model. The result showed that the accuracy of model prediction was 87.5\%, indicated it was feasible to diagnose the dust stress intensity with hyperspctral technology.},
booktitle = {2015 {Fourth} {International} {Conference} on {Agro}-{Geoinformatics} ({Agro}-geoinformatics)},
author = {Liang, L. and Li, Y. and Sun, Q. and Chen, Q. and Di, L. and Deng, M.},
month = jul,
year = {2015},
keywords = {Accuracy, Agriculture, ASD FieldSpec®3 spectrometer, BP-ANN, Computational modeling, crops, diagnosis model, dust pollution stress diagnosis, dust stress, dust stress intensity, hyperspectra, hyperspectral imaging, Hyperspectral imaging, hyperspectral technology, image processing, PCA, pollution, Pollution, principal component analysis, Principal component analysis, S.Golay smoothing, smoothing methods, standard normal variable, Stress, stress intensity levels, SVM, wheat, wheat leaves},
pages = {192--195},
file = {IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/C493QZLS/7248096.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/4875ALAY/Liang et al. - 2015 - Diagnosis the dust pollution stress of wheat leave.pdf:application/pdf}
}
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The spectra of wheat leaves which suffered different stress intensity levels,including severe stress, moderate stress, mild stress and no stress, were collected by ASD FieldSpec®3 spectrometer, and 32 samples were used for each level. All of the samples were divided randomly into two groups, one group with 96 ones used as calibrated set, and another with 32 ones as validated set. The spectra data were then pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). Using the anterior 6 principal components computed by PCA as the model input variables, and the values of stress level as the output variables, the hyperspectral diagnosis models of dust stress intensity were established. And then the 32 unknown samples in the validated set were predicted by the diagnosis model. The result showed that the accuracy of model prediction was 87.5%, indicated it was feasible to diagnose the dust stress intensity with hyperspctral technology.","booktitle":"2015 Fourth International Conference on Agro-Geoinformatics (Agro-geoinformatics)","author":[{"propositions":[],"lastnames":["Liang"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Li"],"firstnames":["Y."],"suffixes":[]},{"propositions":[],"lastnames":["Sun"],"firstnames":["Q."],"suffixes":[]},{"propositions":[],"lastnames":["Chen"],"firstnames":["Q."],"suffixes":[]},{"propositions":[],"lastnames":["Di"],"firstnames":["L."],"suffixes":[]},{"propositions":[],"lastnames":["Deng"],"firstnames":["M."],"suffixes":[]}],"month":"July","year":"2015","keywords":"Accuracy, Agriculture, ASD FieldSpec®3 spectrometer, BP-ANN, Computational modeling, crops, diagnosis model, dust pollution stress diagnosis, dust stress, dust stress intensity, hyperspectra, hyperspectral imaging, Hyperspectral imaging, hyperspectral technology, image processing, PCA, pollution, Pollution, principal component analysis, Principal component analysis, S.Golay smoothing, smoothing methods, standard normal variable, Stress, stress intensity levels, SVM, wheat, wheat leaves","pages":"192–195","file":"IEEE Xplore Abstract Record:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/C493QZLS/7248096.html:text/html;IEEE Xplore Full Text PDF:/Volumes/mini-disk1/Google Drive/_lib/zotero/storage/4875ALAY/Liang et al. - 2015 - Diagnosis the dust pollution stress of wheat leave.pdf:application/pdf","bibtex":"@inproceedings{liang_diagnosis_2015,\n\ttitle = {Diagnosis the dust pollution stress of wheat leaves based on hyperspectral technology},\n\tdoi = {10.1109/Agro-Geoinformatics.2015.7248096},\n\tabstract = {Wheat leaves under different dust stress intensity were diagnosed with hyperspctrectral technology. A four-level dust stress experiment was conducted using the wheat in growing season as the study object. The spectra of wheat leaves which suffered different stress intensity levels,including severe stress, moderate stress, mild stress and no stress, were collected by ASD FieldSpec®3 spectrometer, and 32 samples were used for each level. All of the samples were divided randomly into two groups, one group with 96 ones used as calibrated set, and another with 32 ones as validated set. The spectra data were then pretreated by the methods of S.Golay smoothing and standard normal variable (SNV), and then the pretreated spectra data were analyzed with principal component analysis (PCA). Using the anterior 6 principal components computed by PCA as the model input variables, and the values of stress level as the output variables, the hyperspectral diagnosis models of dust stress intensity were established. And then the 32 unknown samples in the validated set were predicted by the diagnosis model. 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