基于无人机高光谱遥感的柑橘黄龙病植株的监测与分类
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摘要
柑橘黄龙病(Huanglongbing,HLB)是柑橘产业的毁灭性病害,及早发现并挖除病株是防治HLB的有效手段。通过无人机低空遥感监测大面积果园,可大大减少HLB排查工作量和劳动力。该文获取了无人机低空柑橘果园的高光谱影像,分别提取并计算健康和感染HLB植株冠层的感兴趣区域的平均光谱,并对初始光谱进行Savitzky-Golay平滑、异常数据剔除和光谱变换,得到原始光谱、一阶导数光谱和反对数光谱3种光谱,对这3种光谱采用主成分分析法进行降维,与全波段信息比较,分别采用k近邻(kNN)和支持向量机(SVM)进行建模和分类。结果表明,以二次核SVM判别模型对全波段一阶导数光谱的分类准确率达到94.7%,对测试集的误判率为3.36%。表明低空高光谱遥感监测HLB的手段具有可行性,可大大提高果园管理效率和政府防控病情力度。
Citrus Huanglongbing(HLB) is an extremely destructive disease without cured medicine.Finding citrus trees suffering from HLB as soon as possible and eradicating the infected tree timely is an effective measure to prevent citrus from HLB.For large-scale citrus orchards,monitoring HLB is a heavy workload that requires a lot of time and effort.-Using remote sensing by unmanned aerial vehicle(UAV) to monitor the citrus orchards is a feasible measure which could reduce a lot of work and cost.In this study,a hyperspectral image of citrus in orchard was obtained by a UAV equipped with hyperspectral camera,flying at a height of 60 m.26 healthy trees and 26 trees infected HLB were selected from the hyperspectral image,which was radiational corrected and geometric corrected.10 regions of interest(ROIs) were created(5×5 pixel size) on each selected citrus canopy and the mean reflectance spectra in every ROI was calculated.The abnormal spectra were removed by observing the mean reflectance spectra,and the remaining spectra were smoothed and denoised by Savitzky-Golay.The ground reflectance spectra captured by ASD FieldSpec HandHeld 2 Spectroradiometer was used as a reference to verify the effect of the spectra by hyperspectral camera in UAV and it was found that the reflectance spectra of hyperspectral camera had a same trend with the spectra from gound.The first-orderderivative reflectance spectra(FDR) and the inverse logarithmtic reflectance spectra(ILR) were obtained by spectral transformation.The dataset was divided into a training set and a test set by a ratio of 3:1,and the training set was used to train the discriminant model.In the training phase of the model,K-folds cross validation was used internally.Finally,the test data was used to predict in the discriminant model.The k-Nearest Neighbor(kNN) and support vector machine(SVM) model were adopted as classifiers respectively,and 3 kinds of spectra transformed from the full-band spectra and first 3 principal components after PCA were compared as input variables to establish the discriminant model.Different input variables in different classifiers,different kernels in SVM model and different distance calculating way in the kNN were compared.The parameters of different models were gradually tried,and the parameters with the highest training accuracy were selected for modeling.Some conclusions were gotten in the paper.First,the reflectance spectra acquired by remote sensing by UAV could be used to establish the discriminant model for trees injected HLB after a series of processing.Such as the SVM classification model with the quadratic kernel had a classification accuracy of 94.7% for full-band FDR and the predictive error rate for the test data was 3.36%.Second,spectral variable obtained by spectral transformation can improve the classification accuracy of the model.For example,the classification accuracy with FDR as the input variables was the highest in each model.Third,principal component analysis(PCA) dimensionality reduction on spectral variables can significantly improve the recognition speed.We could find that the error rate had decreased for model with ILR after PCA and increased for models with other spectra after PCA.Last but not least,in the SVM classification model with the quadratic kernel,the discriminative accuracy for healthy plants was 100%,and the plants with misjudged just were plants injected HLB,the impact of this result was likely to come from part with HLB in the canopy of fruit trees.In summary,hyperspectral remote sensing by UAV was used to monitor the cultivation of orchards in large areas.It was an effective management method to monitor citrus HLB by establishing a discriminant model.
Citrus Huanglongbing(HLB) is an extremely destructive disease without cured medicine.Finding citrus trees suffering from HLB as soon as possible and eradicating the infected tree timely is an effective measure to prevent citrus from HLB.For large-scale citrus orchards,monitoring HLB is a heavy workload that requires a lot of time and effort.-Using remote sensing by unmanned aerial vehicle(UAV) to monitor the citrus orchards is a feasible measure which could reduce a lot of work and cost.In this study,a hyperspectral image of citrus in orchard was obtained by a UAV equipped with hyperspectral camera,flying at a height of 60 m.26 healthy trees and 26 trees infected HLB were selected from the hyperspectral image,which was radiational corrected and geometric corrected.10 regions of interest(ROIs) were created(5×5 pixel size) on each selected citrus canopy and the mean reflectance spectra in every ROI was calculated.The abnormal spectra were removed by observing the mean reflectance spectra,and the remaining spectra were smoothed and denoised by Savitzky-Golay.The ground reflectance spectra captured by ASD FieldSpec HandHeld 2 Spectroradiometer was used as a reference to verify the effect of the spectra by hyperspectral camera in UAV and it was found that the reflectance spectra of hyperspectral camera had a same trend with the spectra from gound.The first-orderderivative reflectance spectra(FDR) and the inverse logarithmtic reflectance spectra(ILR) were obtained by spectral transformation.The dataset was divided into a training set and a test set by a ratio of 3:1,and the training set was used to train the discriminant model.In the training phase of the model,K-folds cross validation was used internally.Finally,the test data was used to predict in the discriminant model.The k-Nearest Neighbor(kNN) and support vector machine(SVM) model were adopted as classifiers respectively,and 3 kinds of spectra transformed from the full-band spectra and first 3 principal components after PCA were compared as input variables to establish the discriminant model.Different input variables in different classifiers,different kernels in SVM model and different distance calculating way in the kNN were compared.The parameters of different models were gradually tried,and the parameters with the highest training accuracy were selected for modeling.Some conclusions were gotten in the paper.First,the reflectance spectra acquired by remote sensing by UAV could be used to establish the discriminant model for trees injected HLB after a series of processing.Such as the SVM classification model with the quadratic kernel had a classification accuracy of 94.7% for full-band FDR and the predictive error rate for the test data was 3.36%.Second,spectral variable obtained by spectral transformation can improve the classification accuracy of the model.For example,the classification accuracy with FDR as the input variables was the highest in each model.Third,principal component analysis(PCA) dimensionality reduction on spectral variables can significantly improve the recognition speed.We could find that the error rate had decreased for model with ILR after PCA and increased for models with other spectra after PCA.Last but not least,in the SVM classification model with the quadratic kernel,the discriminative accuracy for healthy plants was 100%,and the plants with misjudged just were plants injected HLB,the impact of this result was likely to come from part with HLB in the canopy of fruit trees.In summary,hyperspectral remote sensing by UAV was used to monitor the cultivation of orchards in large areas.It was an effective management method to monitor citrus HLB by establishing a discriminant model.
引文
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[2]柏自琴,周常勇.柑橘黄龙病病原分化及发生规律研究进展[J].中国农学通报,2012,28(1):133-137.Bai Ziqin,Zhou Changyong.The research progress of citrus Huanglongbing on pathogen diversity and epidemiology[J].Chinese Agricultural Science Bulletin,2012,28(1):133-137.(in Chinese with English abstract)
[3]Rosales R,Burns J K.Phytohormone changes and carbohydrate status in sweet orange fruit from Huanglongbinginfected trees[J].Journal of Plant Growth Regulation,2011,30(3):312-321.
[4]Shokrollah H,Abdullah T L,Sijam K,et al.Identification of physical and biochemical characteristic of mandarin(Citrus reticulata)fruit infected by Huanglongbing(HLB)[J].Australian Journal of Crop Science,2011,5(2):181-186.
[5]袁亦文,戈丽清,王德善,等.柑橘黄龙病对柑橘产量和品质的影响[J].浙江农业科学,2007(1):87-88.
[6]鹿连明,范国成,胡秀荣,等.田间柑橘植株不同部位黄龙病菌的PCR检测及发病原因分析[J].植物保护,2011,37(2):45-49.Lu Lianming,Fan Guocheng,Hu Xiurong,et al.PCRdetection of Huanglongbing pathogen in different parts of citrus plants in the field and analysis of the cause of the disease[J].Plant Protection,2011,37(2):45-49.(in Chinese with English abstract)
[7]Deng Xiaoling,Gao Yidi,Chen Jianchi,et al.Curent situation of"candidatus liberibacter asiaticus"in Guangdong,China,where citrus Huanglongbing was first described[J].Journal of Integrative Agriculture,2012,11(3):424-429.
[8]邹敏,宋震,唐科志,等.柑橘黄龙病病原DNA微量提取方法比较[J].植物检疫,2005,19(5):271-274.Zou Min,Song Zhen,Tang Kezhi,et al.Comparing of micro extraction methods of DNA from citrus Huanglongbing pathogen[J].Phytosanitary Diseases,2005,19(5):271-274.(in Chinese with English abstract)
[9]罗志达,叶自行,许建楷,等.柑桔黄龙病的田间诊断方法[J].广东农业科学,2009(3):91-93.
[10]王爱民,邓晓玲.柑桔黄龙病诊断技术研究进展[J].广东农业科学,2008(6):101-103.
[11]王春梅.湖南柑橘黄龙病检测及DNA片段克隆[D].长沙:湖南农业大学,2006.Wang Chunmei.Detection of Citrus Huanglongbing Pathogen in Hunan Province and Cloning[D].Changsha:Hunan Agricultural University,2006.(in Chinese with English abstract)
[12]Planet P,Jagoueix S,BovéJ M,et al.Detection and characterization of the African citrus greening Liberobacter by amplification,cloning,and sequencing of the rpl KAJL-rpo BC operon[J].Current Microbiology,1995,30(3):137-141.
[13]李德望,唐伟文,范怀忠.柑桔黄龙病的血清学检测与诊断方法的初步研究[J].华南农业大学学报,1992,13(2):16-22.Li Dewang,Tang Weiwen,Fan Huaizhong.Preliminary studies on the methods of rapid serological detection and diagnosis of the BLO associated with citrus shoot-yellowing[J].Journal of South China Agricultural University,1992,13(2):16-22.(in Chinese with English abstract)
[14]孙旭东,刘燕德,肖怀春,等.正常、缺素和黄龙病柑桔叶片高光谱成像快速诊断[J].光谱学与光谱分析,2017,37(2):551-556.Sun Xudong,Liu Yande,Xiao Huaichun,et al.Rapid diagnosis of sound,yellow and citrus greening leaves with hyperspectral imaging[J].Spectroscopy and Spectral Analysis,2017,37(2):551-556.(in Chinese with English abstract)
[15]Pereira F M V,Pereira-Filho E R,Venancio A L,et al.Laser-induced fluorescence imaging method to monitor citrus greening disease[J].Computers&Electronics in Agriculture,2011,79(1):90-93.
[16]Pérez M R,Mendoza M G,Elías M G,et al.Raman spectroscopy an option for the early detection of citrus Huanglongbing[J].Applied Spectroscopy,2016,70(5):829-839.
[17]Sankaran S,Ehsani R.Visible-near infrared spectroscopy based citrus greening detection:Evaluation of spectral feature extraction techniques[J].Crop Protection,2011,30(11):1508-1513.
[18]刘燕德,肖怀春,孙旭东,等.基于高光谱成像的柑橘黄龙病无损检测[J].农业机械学报,2016,47(11):231-238.Liu Yande,Xiao Huaichun,Sun Xudong,et al.Nondestructive detection of citrus huanglong disease using hyperspectral image technique[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(11):231-238.(in Chinese with English abstract)
[19]Deng Xiaoling,Kong Chen,Wu Weibin,et al.Detection of citrus Huanglongbing based on principal component analysis and back propagation neural network[J].Acta Photonica Sinica,2014,43(4):10-16
[20]Deng Xiaoling,Li Zhen,Deng Xiaoling,et al.Citrus disease recognition based on weighted scalable vocabulary tree[J].Precision Agriculture,2014,15(3):321-330.
[21]Deng Xiaoling,Lan Yubin,Hong Tiansheng,et al.Citrus greening detection using visible spectrum imaging and C-SVC[J].Computers&Electronics in Agriculture,2016,130:177-183.
[22]梅慧兰,邓小玲,洪添胜,等.柑橘黄龙病高光谱早期鉴别及病情分级[J].农业工程学报,2014,30(9):140-147.Mei Huilan,Deng Xiaoling,Hong Tiansheng,et al.Early detection and grading of citrus Huanglongbing using hyperspectral imaging technique[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2014,30(9):140-147.(in Chinese with English abstract)
[23]Huang Y B,Thomson S J,Hoffmann W C,et al.Development and prospect of unmanned aerial vehicle technologies for agricultural production management[J].International Journal of Agricultural&Biological Engineering,2013,6(3):1-10.
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