基于高光谱微分指数监测春玉米大斑病的研究
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摘要
利用人工接种病菌的方式诱发不同等级春玉米大斑病,在不同生育期测定正常种植区春玉米和不同染病种植区春玉米冠层的高光谱数据。为了有效监测和控制春玉米大斑病的影响,本研究以抽雄期的春玉米冠层高光谱数据为基础,提出了一个新的遥感监测指数,该指数为绿边核心区内一阶微分总和与红边核心区内一阶微分总和的乘积。最后,该监测指数与病害程度的相关性分析结果表明:本研究提出的监测指数与病害程度具有显著地线性相关性,相关系数r2=0.9711,优于常用监测指数与病情之间的相关性,能识别出健康与病害作物,且还可实现作物不同病害严重程度的划分。可见,高光谱遥感方式可进行作物病害监测,对提高粮食产量和保障粮食安全亦具有重要的参考价值。
Spring corn leaf blight with different grades was induced by artificial inoculation of pathogenic bacteria, the hyperspectral data of spring corn canopies in both healthy planting area and different infected planting areas were measured at different growth stages. To effectively monitor and control the effects of spring corn leaf blight, this study proposed a new remote sensing monitoring index which was the product of the sum total of the first derivative within green edge core area and the sum total of the first derivative within red edge core area. Finally, the results of correlation analysis between the proposed monitoring index and degrees of disease showed that the proposed index had significant linear correlation with the degrees of disease, and the correlation coefficient was 0.9711, which was better than the results of other traditional remote sensing monitoring index. The proposed index could be used for recognition of healthy spring corn and infected spring corn, and also could be used for spring corn classification of different disease grades. Therefore, hyperspectral remote sensing method could be used for disease monitoring of spring corn, and has great reference for improving grain output and ensuring grain security.
Spring corn leaf blight with different grades was induced by artificial inoculation of pathogenic bacteria, the hyperspectral data of spring corn canopies in both healthy planting area and different infected planting areas were measured at different growth stages. To effectively monitor and control the effects of spring corn leaf blight, this study proposed a new remote sensing monitoring index which was the product of the sum total of the first derivative within green edge core area and the sum total of the first derivative within red edge core area. Finally, the results of correlation analysis between the proposed monitoring index and degrees of disease showed that the proposed index had significant linear correlation with the degrees of disease, and the correlation coefficient was 0.9711, which was better than the results of other traditional remote sensing monitoring index. The proposed index could be used for recognition of healthy spring corn and infected spring corn, and also could be used for spring corn classification of different disease grades. Therefore, hyperspectral remote sensing method could be used for disease monitoring of spring corn, and has great reference for improving grain output and ensuring grain security.
引文
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[3]Zhang J C,Pu R L,Huang W J,et al.Using in-situ hyperspectral data for detecting and discriminating yellow rust disease from nutrient stresses[J].Field Crops Research,2012,134:165-174.
[4]Cheng T,Rivard B,Sanchez-Azofeifa A,et al.Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation[J].Remote Sensing of Environment,2010,114(4):899-910.
[5]Zhang J C,Luo J H,Huang W J,et al.Continuous wavelet analysis based spectral feature selection for winter wheat yellow rust detection[J].Intelligent Automation and Soft Computing,2011,17(5):531-540.
[6]Yang C M,Cheng C H,Chen R K.Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder[J].Crop Science,2007,47(1):329-335.
[7]Liu Z Y,Wu H F,Huang J F.Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis[J].Computers and Electronics in Agriculture,2010,72(2):99-106.
[8]Graeff S,Link J,Claupein W.Identifi cation of powdery mildew(Erysiphe graminis sp.tritici)and take-all disease(Gaeumannomyces graminis sp.tritici)in wheat(Triticum aestivum L.)by means of leaf refl ectance measurements[J].Central European Journal of Biology,2006,1(2):275-288
[9]刘良云,黄木易,黄文江,等.利用多时相的高光谱航空图像监测冬小麦条锈病[J].遥感学报,2004,8(3):275-281
[10]Moshou D,Bravo C,Oberti R,et al.Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps[J].Real-Time Imaging,2005,11(2):75-83.
[11]Zhang M H,Qin Z H,Liu X.Remote sensed spectral imagery to detect late blight in field tomatoes[J].Precision Agriculture,2005,6(6):489-508.
[12]Xu H R,Ying Y B,Fu X P,et al.Near-infrared spectroscopy in detection leaf miner damage on tomato leaf[J].Biosystems Engineering,2007,96(4):447-454.
[13]Jones C D,Jones J B,Lee W S.Diagnosis of bacterial spot of tomato using spectral signatures[J].Computers and Electronics in Agriculture,2010,74(2):329-335.
[14]Naidu R A,Perry E M,Pierce F J,et al.The potential of spectral reflectance technique for the detection of Grapevine leafrollassociated virus-3in two red-berried wine grape cultivars[J].Computers and Electronics in Agriculture,2009,66(1):38-45.
[15]Huang,J F,Apan,A.Detection of sclerotinia rot disease on celery using hyperspectal data and partial least squares regression[J].Journal of Spatial Science,2006,52(2):129-142.
[16]Zhao C J,Huang M Y,Huang W J,et al.Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models[J].IGARSS,2004,6,4318-4320..
[17]Bravo C,Moshou D,West J S,et al.Early disease detection in wheat fields using spectral reflectance[J].Biosystems Engineering,2003,84(2):137-145.
[18]Steddom K,Heidel G,Jones D.Remote detection of rhizomania in sugar beets[J].Phytopathology,2003,93(6):720-726.
[19]蒋金豹,陈云浩,黄文江,等.用高光谱微分指数监测冬小麦病害的研究[J].光谱学与光谱分析,2007,27(12):2475-2479.
[20]刘占宇,黄敬峰,陶荣祥,等.基于主成分分析和径向基网络的水稻胡麻斑病严重度估测[J].光谱学与光谱分,2008,28(9):2156-2160.