Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r ² = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r ² = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r ² = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.     

小麦条锈病高光谱遥感监测技术研究

利用ASD地面非成像光谱仪对小麦条锈病进行了单片病叶及冠层的光谱特征研究。结果表明,健康、发病及处于潜育期的小麦植株在某些特定波段的光谱反射率存在显著差异;并且不同孢子堆密度的小麦单片病叶的反射光谱曲线也存在相应差异;对小麦条锈病冠层光谱的研究发现,在930nm附近,病情指数(y)与冠层光谱反射率(x)的相关性达到了极显著水平,二者之间的回归模型为:y=-2.5173x+1.2217(R2=0.9484)。     

冬小麦条锈病生理变化及其遥感机理

对不同处理条件下的冬小麦条锈病进行 (病情指数 ) (DI)调查 ,并进行同步的光谱测定及田间取样 ,在室内测试了对病情指数有重要影响的几个参数因子 ,叶绿素含量及上叶含水量 ,并且将其与光谱反射率进行统计分析。研究结果表明 ,这些参数因子与反射率数据在 5 5 0～ 70 0和 70 0～ 1160nm范围内与DI有着相似的高相关性 ,说明条锈病害的DI变化可以通过叶绿素含量、上叶含水量参数直接的变化在光谱上得到响应 ,从而证明遥感监测DI是可行的 ,同时解释了遥感监测机理。选出与叶绿素含量、上叶含水量相关性最强的波段与DI作多元回归 ,建立的模型能很好地反演冬小麦条锈病的病情指数 ,正确率达到 75 %以上     

Spectral signatures of sugar beet leaves for the detection and differentiation of diseases

This study examines the potential of hyperspectral sensor systems for the non-destructive detection and differentiation of plant diseases. In particular, a comparison of three fungal leaf diseases of sugar beet was conducted in order to facilitate a simplified and reproducible data analysis method for hyperspectral vegetation data. Reflectance spectra (400–1050 nm) of leaves infected with the fungal pathogens Cercospora beticola, Erysiphe betae, and Uromyces betae causing Cercospora leaf spot, powdery mildew and rust, respectively, were recorded repeatedly during pathogenesis with a spectro-radiometer and analyzed for disease-specific spectral signatures. Calculating the spectral difference and reflectance sensitivity for each wavelength emphasized regions of high interest in the visible and near infrared region of the spectral signatures. The best correlating spectral bands differed depending on the diseases. Spectral vegetation indices related to physiological parameters were calculated and correlated to the severity of diseases. The spectral vegetation indices Normalised Difference Vegetation Index (NDVI), Anthocyanin Reflectance Index (ARI) and modified Chlorophyll Absorption Integral (mCAI) differed in their ability to assess the different diseases at an early stage of disease development, or even before first symptoms became visible. Results suggested that a distinctive differentiation of the three sugar beet diseases using spectral vegetation indices is possible using two or more indices in combination.     

Airborne Hyperspectral Imagery and Yield Monitor Data for Mapping Cotton Yield Variability

Increased availability of hyperspectral imagery necessitates the evaluation of its potential for precision agriculture applications. This study examined airborne hyperspectral imagery for mapping cotton (Gossypium hirsutum L.) yield variability as compared with yield monitor data. Hyperspectral images were acquired using an airborne imaging system from two cotton fields during the 2001 growing season, and yield data were collected from the fields using a cotton yield monitor. The raw hyperspectral images contained 128 bands between 457 and 922 nm. The raw images were geometrically corrected, georeferenced and resampled to 1 m resolution, and then converted to reflectance. Aggregation functions were then applied to each of the 128 bands to reduce the cell resolution to 4 m (close to the cotton picker's cutting width) and 8 m. The yield data were also aggregated to the two grids. Correlation analysis showed that cotton yield was significantly related to the image data for all the bands except for a few bands in the transitional range from the red to the near-infrared region. Stepwise regression performed on the yield and hyperspectral data identified significant bands and band combinations for estimating yield variability for the two fields. Narrow band normalized difference vegetation indices derived from the significant bands provided better yield estimation than most of the individual bands. The stepwise regression models based on the significant narrow bands explained 61% and 69% of the variability in yield for the two fields, respectively. To demonstrate if narrow bands may be better for yield estimation than broad bands, the hyperspectral bands were aggregated into Landsat-7 ETM+ sensor's bandwidths. The stepwise regression models based on the four broad bands explained only 42% and 58% of the yield variability for the two fields, respectively. These results indicate that hyperspectral imagery may be a useful data source for mapping crop yield variability.     

基于遥感技术的不同施氮水平下小麦条锈病病情反演研究

为构建不同施氮条件下,小麦条锈病病情光谱反演模型,设置了在不同氮素水平条件下接种小麦条锈病,将菌情指数与植被指数、一阶微分参数进行回归分析,构建抽穗期、开花期、灌浆期、乳熟期共5个模型。为了评估施氮量对病情反演模型的影响,在模型中加入氮素因子,模型病情反演预测效果表明,抽穗期模型加入氮素因子后预测效果有所提高,抽穗期的模型1-1(R2=0.392 8,P=0.005 4)、1-2(R2=0.449 8,P=0.011 3)、2-2(R2=0.573 3,P=0.001 7)预测效果较好且较稳定,开花期、灌浆期、乳熟期模型预测效果不理想。本研究结果表明,可以利用植被指数、一阶微分参数较好反演抽穗期小麦条锈病病情,加入氮素因子后预测效果有所提高,说明氮素因子对病情反演有影响。     

甘肃省小麦品种(系)抗条锈性鉴定及评价

对甘肃省40个小麦品种(系)进行抗条锈性鉴定。结果表明:全生育期抗病的参试材料较少,在苗期感病的许多材料,在成株期抗病,并且反应型非常低,说明小麦不同生育阶段抗性存在差异;而且成株期绝大部分品种(系)的严重度、普遍率及病情指数均低于苗期。并针对本研究结果对今后品种抗条锈鉴定、抗锈育种及现有品种的生产应用所涉及的有关问题进行了探讨。     

水稻穗颈瘟严重度的高光谱反演模型研究

通过人工诱发不同等级水稻穗颈瘟,测定染病稻穗光谱及其病情指数(DI).分析了病穗反射光谱和一阶微分在绿光区、黄光区和近红外区反射特征差异,将病情指教及光谱数据进行相关分析,建立水稻穗颈瘟严重度的估测模型并进行可靠性检验.结果表明:750-850 nm反射光谱及720-760nm 一阶微分为水稻穗颈瘟的敏感波段;在400-700nm光谱范围内病情指数与光谱反射率的相关性达到了极显著正相关,对于微分光谱,在520-540nm和710-760nm光谱范围内病情指数与光谱反射率的相关性达到了极显著负相关,在570-610nm光谱范围内病情指数与光谱反射率的相关性达到了极显著正相关,并在此基础上建立了水稻穗颈瘟病情指数的高光谱反演模型,该研究为今后通过航空、航天遥感大面积监测水稻穗颈瘟提供了理论依据.     

Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers

Hyperspectral signatures can provide abundant information regarding health status of crops; however it is difficult to discriminate between biotic and abiotic stress. In this study, the case of simultaneous occurrence of yellow rust disease symptoms and nitrogen stress was investigated by using hyperspectral features from a ground based hyperspectral imaging system. Hyperspectral images of healthy and diseased plant canopies were taken at Rothamsted Research, UK by a Specim V10 spectrograph. Five wavebands of 20 nm width were utilized for accurate identification of each of the stress and healthy plant conditions. The technique that was developed used a hybrid classification scheme consisting of hierarchical self organizing classifiers. Three different architectures were considered: counter-propagation artificial neural networks, supervised Kohonen networks (SKNs) and XY-fusion. A total of 12 120 spectra were collected. From these 3 062 (25.3%) were used for testing. The results of biotic and abiotic stress identification appear to be promising, reaching more than 95% for all three architectures. The proposed approach aimed at sensor based detection of diseased and stressed plants so that can be treated site specifically contributing to a more effective and precise application of fertilizers and fungicides according to specific plant’s needs.     

基于光谱与图像信息的杏鲍菇多糖含量检测

【目的】利用高光谱成像技术实现杏鲍菇Pleurotus eryngii多糖含量的快速无损检测。【方法】利用高光谱图像采集系统获取350～1 021 nm波长范围内的杏鲍菇高光谱图像,同时利用苯酚–硫酸法测定对应样本的多糖含量。通过波段运算和阈值分割构建掩膜图像,使样本与背景相分离。采用主成分分析(PCA)处理原始高光谱图像,获得代表原始图像99%信息的2个主成分图像(PC1、PC2),然后利用连续投影算法(SPA)选出554.4、772.8、811.4、819.1、855.6、986.3和1 019.5 nm 7个特征波长及对应的光谱特征,分别提取7个特征波长图像和2个主成分图像的纹理与颜色特征,最后利用偏最小二乘回归(PLSR)建立杏鲍菇样本基于不同图像特征与多糖含量之间的关系模型。【结果】从校正集决定系数(Rc2)来看,基于特征光谱+特征波长图像特征+主成分图像特征的模型效果最好,Rc2=0.954,RMSEc=0.341;从预测集决定系数Rp2来看,基于特征光谱+特征波长图像特征的模型效果最好,Rp2=0.868,RMSEP=0.539。【结论】该研究结果可为杏鲍菇多糖含量的快速、无损检测提供一定的参考。     

结合多尺度纹理特征的高光谱影像面向对象树种分类

目的基于机载高光谱影像的分类研究中，利用不同尺度纹理特征与面向对象分类相结合的方法在树种分类的研究中应用较少，并且相关研究主要针对单一树种识别而不考虑多种树种，因此对于复杂林分中的树种识别能力有待进一步研究。本研究拟探究不同尺度纹理特征结合面向对象的分类技术在树种精细分类中的应用效果。方法利用机载高光谱数据进行面向对象的树种精细分类。根据研究区内地表类型情况，采用分层分类的方法区分非林地、其他林地与有林地，对有林地进行树种的精细分类。从机载高光谱图像中提取特征变量，包括独立主成分分析ICA变换光谱特征以及空间纹理特征，分析各树种的光谱反射率及所适合的纹理尺度，依据不同尺度纹理特征进行分层分类，比较不同特征利用支持向量机SVM分类的树种分类结果。结果结合单一尺度纹理特征的分类结果总体精度为87.11%，Kappa系数为0.846；结合不同尺度纹理特征的分类总体精度为89.13%，Kappa系数为0.87，相比于仅利用光谱特征的分类精度分别提升了4.03%和6.05%。说明在面向对象的分类中，纹理特征的加入对于提升树种分类的精度具有显著效果。结合不同尺度纹理特征的树种分类精度要高于单一尺度纹理特征的分类精度，尤其在其他阔叶树种和马尾松树种的分类中，制图精度较单一纹理尺度分别提高了5.48%和6.12%。结论利用不同尺度的纹理特征分类比单一尺度纹理特征分类更具优势，提高了纹理特征在树种分类中的贡献率；综合利用机载高光谱影像的光谱特征和不同尺度纹理特征的面向对象分类方法，使得树种识别更为精细和准确。该方法对于复杂林分树种的分类是有效的，能够满足机载高光谱影像树种精细识别的应用需求。      

Molecular Mapping of Two Novel Stripe Rust Resistant Genes YrTp1 and YrTp2 in A-3 Derived from Triticum aestivum × Thinopyrum ponticum

Loss of variety resistance to stripe rust (Puccinia striiformis Westend f. sp.tritici) is an important factor causing massive periodical epidemic of rust in wheat production. Creation and development of new races of rust pathogen have led to serious crisis of resistance loss in widely planted varieties. This has quickened the search for new resistance resources. Molecular marker could facilitate the identification of the location of novel genes. A line A-3 with high resistance (immune) to currently epidemic yellow rust races (CY29, 31, 32) was screened out in offspring of Triticum aestivum × Thinopyrum ponticum. Segregation in F₂ and BC₁ populations indicated that the resistance was controlled by two independent genes: one dominant and one recessive. SSR markers were employed to map the two resistant genes in the F₂ and BC₁ populations. A marker WMC477-_167bp located on 2BS was linked to the dominant gene with genetic distance of 0.4 cM. Another marker WMC364-_{208 bp} located on 7BS was linked to the recessive-resistant gene with genetic distance of 5.8 cM. The two genes identified in this paper might be two novel stripe rust resistant genes, which were temporarily designated as YrTp1 and YrTp2, respectively. The tightly linking markers facilitate transfer of the two resistant genes into the new varieties to control epidemic of yellow rust.     

Analysis of spectral difference between the foreside and backside of leaves in yellow rust disease detection for winter wheat

Disease detection by means of hyperspectral reflectance is inevitably influenced by the spectral difference between foreside (adaxial surface) and backside (abaxial surface) of a leaf. Taking yellow rust disease in winter wheat as an example, the spectral differences between the foreside and backside of healthy and diseased wheat leaves at both jointing stage and grain filling stage were investigated based on spectral measurements with a large sample size. The spectral difference between leaf orientations was found to be confused with disease signals to some extent. Firstly, the original bands and spectral features (SFs) that were sensitive to the disease were identified through a correlation analysis. Then, to eliminate the influence of leaf orientation, a pairwise t test was used to screen for the orientation insensitive bands and SFs. By conducting an overlapping procedure, the bands/SFs that were sensitive to the disease yet insensitive to the leaf orientations were selected and tested for disease detection. The results suggested that the Ref_{525–745 nm}, Ref_{1060–1068 nm}, DEP920–1120, DEP1070–1320, AREA1070–1320, SR and NDVI at the jointing stage, and the Ref_{606–697 nm}, Ref_{740–752 nm}, WID550–770, SR, NDVI, GNDVI, RDVI, GI and MCARI at the grain filling stage were capable of eliminating the influence of leaf orientation, and were retained for disease detection. Given these features, models based on the partial least square regression analysis showed a better performance at the grain filling stage, with the R ² of 0.854 and RMSE of 0.104. This result indicated that reliable estimation of disease severity can be made until the grain filling stage. In the future, more attention should be given to leaf orientation when detecting disease at the canopy level.     

水稻纹枯病不同分级标准病情指数间的关系

研究了水稻纹枯病不同分级标准的病情指数间的关系以及病株率与病情指数间的相互关系。结果表明;按9级标准计算的病指与按4级或5级标准计算的病指间的关系均可用线性函数描述。在病指<0.07时,按9级标准计算的病指与按国际9级标准计算的病指间的关系也呈线性关系;当病指>0.07时,呈幂函数关系。病株率与病指间的关系呈幂函数关系。分别建立了这些关系的数学模型,经检验结果基本合理可靠。本文还讨论了这些关系在病害监测上的应用。     

小麦条锈病产量损失及防治指标研究

试验在小麦自然生长条件下,研究了小麦条锈病病叶率、剑叶严重度与小麦产量损失及其防治指标.结果表明,小麦产量损失率(y)与条锈病病叶率分级值(x)的回归方程为y＝2.36x,相关系数r为0.9873**;与严重度分级值(x)的回归方程为y=5.79x.相关系数r为0.9886".小麦条锈病的防治指标是病叶率为2%或剑叶严重度为1%.     

受条锈病侵染的不同小麦品种发病特点分析

以10个对条锈病抗性不同的冬小麦品种为材料,在条锈病的常发易变区之一的天水连续3年进行了小麦成株期发病情况及其特点的研究,比较了终期严重度和相对病程曲线下面积年度内和年度间的差异和联系.结果表明:各品种的反应型年度间变化不大,严重度和普遍率2003和2004年基本保持稳定,2005年严重度和普遍率较前2年有所上升.相对病程曲线下面积和最终严重度二者相关系数为0.98,说明以后可以通过直接调查最终严重度来鉴定慢锈性.     

两种液肥对小麦条锈病的防病效果

在温室条件下 ,应用喷就发、万民乐两种液肥不同浓度的稀释液对小麦品种铭贤 16 9进行浸种和叶面喷施。结果表明 :①两种液肥浸种均有延缓小麦条锈病病情发展的作用 ,高浓度较低浓度效果好 ,喷就发较万民乐效果好。②喷就发 4 0 0倍、6 0 0倍、80 0倍液浸种后 ,可降低小麦条锈病病情严重度 ,万民乐 4 0 0倍、6 0 0倍液有类似作用。③喷就发 4 0 0倍、6 0 0倍、80 0倍、10 0 0倍液于接菌后喷施可降低发病叶片严重度 ,万民乐 4 0 0倍、80 0倍、10 0 0倍液有类似作用。④喷就发 4 0 0倍、6 0 0倍、80 0倍、10 0 0倍液叶面喷施对条锈病有预防作用 ,万民乐 4 0 0倍、6 0 0倍液有类似作用     

Airborne hyperspectral imagery and linear spectral unmixing for mapping variation in crop yield

Spectral unmixing techniques can be used to quantify crop canopy cover within each pixel of an image and have the potential for mapping the variation in crop yield. This study applied linear spectral unmixing to airborne hyperspectral imagery to estimate the variation in grain sorghum yield. Airborne hyperspectral imagery and yield monitor data recorded from two sorghum fields were used for this study. Both unconstrained and constrained linear spectral unmixing models were applied to the hyperspectral imagery with sorghum plants and bare soil as two endmembers. A pair of plant and soil spectra derived from each image and another pair of ground-measured plant and soil spectra were used as endmember spectra to generate unconstrained and constrained soil and plant cover fractions. Yield was positively related to the plant fraction and negatively related to the soil fraction. The effects of variation in endmember spectra on estimates of cover fractions and their correlations with yield were also examined. The unconstrained plant fraction had essentially the same correlations (r) with yield among all pairs of endmember spectra examined, whereas the unconstrained soil fraction and constrained plant and soil fractions had r-values that were sensitive to the spectra used. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Results showed that the best plant and soil fractions provided better correlations than 96.3 and 99.9% of all the NDVIs for fields 1 and 2, respectively. Since the unconstrained plant fraction could represent yield variation better than most narrow-band NDVIs, it can be used as a relative yield map especially when yield data are not available. These results indicate that spectral unmixing applied to hyperspectral imagery can be a useful tool for mapping the variation in crop yield.     

铜胁迫下玉米光谱变化的奇异性诊断指数与污染甄别

通过研究不同程度铜污染胁迫下玉米光谱奇异性变化特征来诊断玉米受Cu2+污染程度。通过设置不同铜胁迫浓度下的玉米盆栽实验,根据实测的SVC高光谱数据和Cu2+含量数据,采用经验模态分解(EMD)与小波变换相结合的方法提取玉米光谱奇异信息,并构建奇异性诊断指数对玉米光谱奇异性进行定性分析,从而实现玉米铜污染程度的甄别。同时与常规的绿峰高度、红边最大值、红边一阶微分包围面积等植被重金属污染信息监测方法进行比较来验证该方法的有效性。结果显示:奇异性诊断指数(SI)与玉米叶片中Cu2+含量存在较强的相关关系,SI随叶片中Cu2+含量的增加而增大,其相关系数达到0.972 4,从而证明光谱奇异性诊断指数能有效地诊断叶片光谱的奇异性变化及其污染程度,为作物重金属污染监测提供参考依据。     

基于高光谱成像技术的羊肉新鲜度预测

为实现羊肉新鲜度的快速、无损检测,应用高光谱成像技术对不同存储天数的羊肉建立挥发性盐基氮TVB-N(Total volatile basic nitrogen,TVB-N)含量预测模型。通过高光谱成像系统获取羊肉样本935～2 539nm的高光谱图像,选取样本左上、左下、右上、右下和中间5个位置20×20像素的方形作为感兴趣区域(Region of interesting,ROI),提取ROI并计算区域内样本平均光谱。利用二进制粒子群优化算法(Binary particle swarm optimization,BPSO)和相关系数分析法(Correlation coefficient,CC)提取羊肉TVB-N高光谱特征变量,结合偏最小二乘回归(Partial least squares regression,PLSR)和随机森林回归(Random forest regression,RFR)建模算法,分别建立表征羊肉TVB-N含量的BPSO-PLSR、BPSO-RFR、CC-PLSR、CC-RFR预测模型。依据袋外均方根误差RMSEOOB最小原则,对最佳回归子树和分裂特征2个主要参数进行寻优以提高RFR建模算法的预测精度。比较4个模型的预测效果发现,BPSO-RFR模型的预测精度最高,其校正集决定系数R_c~2和均方根误差RMSEC分别为0.87和2.99,预测集决定系数R_p~2和均方根误差RMSEP分别为0.86和3.36。综上,高光谱成像技术和机器学习算法的有机结合为快速有效检测肉品新鲜度提供了理论依据。