VERTICAL LEAF SPECTRAL VARIATION AS AN INDICATOR OF NITROGEN NUTRITION STATUS IN RICE PLANTS

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In situ hyperspectral data analysis for pigment content estimation of rice leaves

Analyses of the correlation between hyperspectral reflectance and pigment content including chlorophyll-a, chlorophyll-b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350-1050 nm and resolution of 3 nm. The chlorophyll-a, chlorophyll-b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorophyll-a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorophyll-b and caroteniod and the spectral variables was far from that of chlorophyll-a. The potential of hyperspectral reflectance measurement for estimating chlorophyll-a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorophyll-a of upper leaves was obtained by using some hyperspectral variables such as SDr, SDb and their integration.     

Vertical leaf spectral variation as an indicator of nitrogen nutrition status in rice plants

A field experiment was conducted to study the response of individual leaf spectral reflectance to five levels of nitrogen fertilizer treatments in rice (Oriza sativa L.) plants. Sampling was combined through a rice canopy at upper, medium and low levels for biomass, nitrogen and water content measurements with spectral signals from the leaves. The vertical gradients of leaf biomass, nitrogen and water contents were associated with the nitrogen availability during tillering, panicle formation, initial heading and heading. Rice plants treated with the lowest rate of N could be characterized with the lowest value of gradient in leaf biomass and leaf water content and the highest value of gradient in leaf N concentration. A spectral gradient of single reflectance (R), ratio (RVI) and normalized difference (ND) of two individual reflectances was defined as this yielded a better relationship between the spectral data and leaf nitrogen concentration. The results suggested the spectral gradients may be used as an improved diagnostic tool for nitrogen status. Project (49771056) supported by Natural Science Foundation of China(NSFC).     

In situ hyperspectral data analysis for pigment content estimation of rice leaves

INTRODUCTIONPigmentsarecloselyrelatedtothephysiolog icalfunctionofleaves.Chlorophyllsabsorblightenergyandtransferitintothephotosyntheticpro cess.Carotenoids (yellowpigments)canalsocontributeenergytothephotosyntheticsystem .Becauseoftheimportanceofpigments…     

In situ hyperspectral data analysis for pigment content estimation of rice leaves

Analyses of the correlation between hyperspectral reflectance and pigment content including chlorophyll-a, chlorophyll-b and carotenoid of leaves in different sites of rice were reported in this paper. The hyperspectral reflectance of late rice during the whole growing season was measured using a Spectroradiometer with spectral range of 350–1050 nm and resolution of 3 nm. The chlorophyll-a, chlorophyll-b and carotenoid contents in rice leaves in rice fields to which different levels of nitrogen were applied were measured. The chlorophyll-a content of upper leaves was well correlated with the spectral variables. However, the correlation between both chlorophyll-b and caroteniod and the spectral variables was far from that of chlorophyll-a. The potential of hyperspectral reflectance measurement for estimating chlorophyll-a of upper leaves was evaluated using univariate correlation and multivariate regression analysis methods with different types of predictors. This study showed that the most suitable estimated model of chlorophyll-a of upper leaves was obtained by using some hyperspectral variables such asSD _r,SD _b and their integration. Project (No. 40171065) supported by the National Natural Science Foundation of China.     

偏最小二乘法用于阿昔洛韦片的近红外漫反射快速、无损含量测定

目的:采用偏最小二乘法结合近红外漫反射光谱,建立阿昔洛韦片的快速无损含量测定模型.方法:以阿昔洛韦片为分析对象,用光纤探头测定近红外漫反射光谱.对光谱进行不同预处理方法建模并进行比较,多元校正模型为偏最小二乘法.结果:在11995.5～4246.7cm-1波长范围内采用一阶导数结合矢量归一化对光谱进行预处理,结果最优.定量模型的浓度范围为27%～53%.预示集平均回收率为98.69%,RSD为4.60%,RMSEP为0.0526.结论:近红外漫反射光谱法快速,简便,无损,能够用于阿昔洛韦片含量测定.     

Optimal waveband identification for estimation of leaf area index of paddy rice

The objectives of the study were to select suitable wavebands for rice leaf area index (LAI) estimation using the data acquired over a whole growing season, and to test the efficiency of the selected wavebands by comparing them with feature positions of rice canopy spectra. In this study, the field experiment in 2002 growing season was conducted at the experimental farm of Zhejiang University, Hangzhou, China. Measurements of hyperspectral reflectance (350-2500 nm) and corresponding LAI were made for a paddy rice canopy throughout the growing season. And three methods were employed to identify the optimal wavebands for paddy rice LAI estimation: correlation coefficient-based method, vegetation index-based method, and stepwise regression method. This research selected 15 wavebands in the region of 350-2500 nm, which appeared to be the optimal wavebands for the paddy rice LAI estimation. Of the selected wavebands, the most frequently occurring wavebands were centered around 554, 675, 723, and 1633 nm. They were followed by 444, 524, 576, 594, 804, 849, 974, 1074, 1219, 1510, and 2194 nm. Most of them made physical sense and had their counterparts in spectral known feature positions, which indicates the promising potential of the 15 selected wavebands for the retrieval of paddy rice LAI.     

比光谱-导数分光光度法在多光谱成像技术上的应用

针对基于Kubelka-Munk理论光谱反射比重建算法中获得浓度矩阵难的技术环节,提出了一种全新的光谱反射比的重建方法——基于比光谱-导数分光光度法的光谱反射比重建算法,并以获取二元混合颜料样本颜色信息为例,证明了此算法的可行性.     

Application Fourier transform near infrared spectrometer in rapid estimation of soluble solids content of intact citrus fruits

INTRODUCTION Soluble solids content (SSC) is a major charac- teristic used for assessing citrus fruit quality. Near-infrared spectroscopy (NIRS) has been used as a rapid and nondestructive technique for determining the soluble solids content of fruit. Kawano et al.(1992) measured sugar content of peaches in the wavelength region of 680~1235 nm. Their experiments indicated good correlation between the NIR spectra and the sugar content (r=0.97, SEP=0.05 °Brix). Slaughter (1995) devel…     

Detection of nitrogen-overfertilized rice plants with leaf positional difference in hyperspectral vegetation index

The main objective of this work was to compare the applicability of the single leaf (the uppermost leaf L1 and the third uppermost leaf L3) modified simple ratio (mSR₇₀₅ index) and the leaf positional difference in the vegetation index between L1 and L3 (mSR_705L1-mSR_705L3) in detecting nitrogen (N)-overfertilized rice plants. A field experiment consisting of three rice genotypes and five N fertilization levels (0, 75, 180, 285, and 390 kg N/ha) was conducted at Xiaoshan, Hangzhou, Zhejiang Province, China in 2008. The hyperspectral reflectance (350–2500 nm) and the chlorophyll concentration (ChlC) of L1 and L3 were measured at different stages. The mSR_705L1 and mSR_705L3 indices appeared not to be highly sensitive to the N rates, especially when the N rate was high (above 180 kg N/ha). The mean mSR_705L1-mSR_705L3 across the genotypes increased significantly (P<0.05) or considerably from 180 to 285 kg N/ha treatment and from 285 to 390 kg N/ha treatment at all the stages. Also, use of the difference (mSR_705L1-mSR_705L3) greatly reduced the influence of the stages and genotypes in assessing the N status with reflectance data. The results of this study show that the N-overfertilized rice plants can be effectively detected with the leaf positional difference in the mSR₇₀₅ index.     

Determination of soluble solid content and acidity of loquats based on FT-NIR spectroscopy

The near infrared (NIR) spectroscopy technique has been applied in many fields because of its advantages of simple preparation,fast response,and non-destructiveness.We investigated the potential of NIR spectroscopy in diffuse reflectance mode for determining the soluble solid content (SSC) and acidity (pH) of intact loquats.Two cultivars of loquats (Dahongpao and Jiajiaozhong) harvested from two orchards (Tangxi and Chun'an,Zhejiang,China) were used for the measurement of NIR spectra between 800 and 2500 nm.A total of 400 loquats (100 samples of each cultivar from each orchard) were used in this study.Relationships between NIR spectra and SSC and acidity of ioquats were evaluated using partial least square (PLS) method.Spectra preprocessing options included the first and second derivatives,multiple scatter correction (MSC),and the standard normal variate (SNV).Three separate spectral windows identified as full NIR (800-2500 nm),short NIR (800～1100 nm),and long NIR (1100～2500 nm) were studied in factorial combination with the preprocessing options.The models gave relatively good predictions of the SSC of loquats,with root mean square error of prediction (RMSEP) values of 1.21,1.00,0.965,and 1.16 °Brix for Tangxi-Dahongpao,Tangxi-Jiajiaozhong,Chun'an-Dahongpao,and Chun'an-Jiajiaozhong,respectively.The acidity prediction was not satisfactory,with the RMSEP of 0.382,0.194,0.388,and 0.361 for the above four loquats,respectively.The results indicate that NIR diffuse reflectance spectroscopy can be used to predict the SSC and acidity of loquat fruit.     

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy

INTRODUCTION Consumers’ acceptance of fresh or processedapples is the ultimate goal of apple breeders, foodscientists and supermarket managers. Internal qualityassessment has focused on two major objectives:removal of fruit with internal defects and taste selec-tion. Three major parameters including sugar content,acidity and firmness have to be taken into account todetermine the internal quality and the taste of an apple.Near infrared spectroscopy has been used to measureseveral properti…     

Prediction of valid acidity in intact apples with Fourier transform near infrared spectroscopy

To develop nondestructive acidity prediction for intact Fuji apples, the potential of Fourier transform near infrared (FT-NIR) method with fiber optics in interactance mode was investigated. Interactance in the 800 nm to 2619 nm region was measured for intact apples, harvested from early to late maturity stages. Spectral data were analyzed by two multivariate calibration techniques including partial least squares (PLS) and principal component regression (PCR) methods. A total of 120 Fuji apples were tested and 80 of them were used to form a calibration data set. The influences of different data preprocessing and spectra treatments were also quantified. Calibration models based on smoothing spectra were slightly worse than that based on derivative spectra, and the best result was obtained when the segment length was 5 nm and the gap size was 10 points. Depending on data preprocessing and PLS method, the best prediction model yielded correlation coefficient of determination (r^2) of 0.759,low root mean square error of prediction (RMSEP) of 0.0677, low root mean square error of calibration (RMSEC) of 0.0562. The results indicated the feasibility of FT-NIR spectral analysis for predicting apple valid acidity in a nondestructive way.     

Combining Principal Component Regression and Artificial Neural Network to Predict Chlorophyll-a Concentration of Yuqiao Reservoir’s Outflow

In order to investigate the eutrophication degree of Yuqiao Reservoir, a hybrid method, combining principal component regression (PCR) and artificial neural network (ANN), was adopted to predict chlorophyll-a concentration of Yuqiao Reservoir’s outflow. The data were obtained from two sampling sites, site 1 in the reservoir, and site 2 near the dam. Seven water variables, namely chlorophyll-a concentration of site 2 at time t and that of both sites 10 days before t, total phosphorus(TP), total nitrogen(TN), dissolved oxygen(DO), and temperature from January 2000 to September 2002, were utilized to develop models. To remove the collinearity between the variables, principal components extracted by principal component analysis were employed as predictors for models. The performance of models was assessed by the square of correlation coefficient, mean absolute error (MAE), root mean square error (RMSE) and average absolute relative error (AARE). Results show that the hybrid method has achieved more accurate prediction than PCR or ANN model. Finally, the three models were applied to predicting the chlorophyll-a concentration in 2003. The predictions of the hybrid method were found to be consistent with the observed values all year round, while the results of PCR and ANN models did not fit quite well from July to October.     

高光谱估测黄壁庄水库悬浮物浓度的试验分析

以黄壁庄水库为试验区,利用ASD手持式光谱辐射计进行了库区水体反射光谱测量和同步水质采样分析,在分析水库水体反射光谱特征的基础上,运用微分光谱技术和统计相关方法直接分析诊断悬浮物浓度（SS）的敏感波段,并建立简单的线性估算模型．结果显示,715nm波段反射率的一阶微分与悬浮物浓度有较好的相关性,为今后利用星载高光谱传感器对黄壁庄水库乃至我国北方内陆水体悬浮物浓度进行大面积反演提供理论基础．     

Application of NIR spectroscopy for firmness evaluation of peaches

The use of near infrared (NIR) spectroscopy was proved to be a useful tool for quality analysis of fruits. A bifurcated fiber type NIR spectrometer, with a detection range of 800～2500 nm by lnGaAs detector, was used to evaluate the firmness of peaches. Anisotropy of NIR spectra and firmness of peaches in relation to detecting positions of different parts (including three latitudes and three longitudes) were investigated. Both spectra absorbency and firmness of peach were influenced by longitudes (i,ii, iii) and latitudes (A, B, C). For modeling, two thirds of the samples were used as the calibration set and the remaining one third were used as the validation or prediction set. Partial least square regression (PLSR) models for different longitude and latitude spectra and for the whole fruit show that collecting several NIR spectra from different longitudes and latitudes of a fruit for NIR calibration modeling can improve the modeling performance. In addition, proper spectra pretreatments like scattering correction or derivative also can enhance the modeling performance. The best results obtained in this study were from the holistic model with multiplicative scattering correction (MSC) pretreatment, with correlation coefficient of cross-validation rcv=0.864, root mean square error of cross-validation RMSECV=6.71 N, correlation coefficient of calibration r=0.948, root mean square error of cali-bration RMSEC=4.21 N and root mean square error of prediction RMSEP=5.42 N. The results of this study are useful for further research and application that when applying NIR spectroscopy for objectives with anisotropic differences, spectra and quality indices are necessarily measured from several parts of each object to improve the modeling performance.     

Characterizing and estimating rice brown spot disease severity using stepwise regression,principal component regression and partial least-square regression

Detecting plant health conditions plays a key role in farm pest management and crop protection. In this study, measurement of hyperspectral leaf reflectance in rice crop (Oryzasativa L.) was conducted on groups of healthy and infected leaves by the fungus Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann) through the wavelength range from 350 to 2 500 nm. The percentage of leaf surface lesions was estimated and defined as the disease severity. Statistical methods like multiple stepwise regression, principal component analysis and partial least-square regression were utilized to calculate and estimate the disease severity of rice brown spot at the leaf level. Our results revealed that multiple stepwise linear regressions could efficiently estimate disease severity with three wavebands in seven steps. The root mean square errors (RMSEs) for training (n=210) and testing (n=53) dataset were 6.5% and 5.8%, respectively. Principal component analysis showed that the first principal component could explain approximately 80% of the variance of the original hyperspectral reflectance. The regression model with the first two principal components predicted a disease severity with RMSEs of 16.3% and 13.9% for the training and testing dataset, respec-tively. Partial least-square regression with seven extracted factors could most effectively predict disease severity compared with other statistical methods with RMSEs of 4.1% and 2.0% for the training and testing dataset, respectively. Our research demon-strates that it is feasible to estimate the disease severity of rice brown spot using hyperspectral reflectance data at the leaf level.     

有测量误差动态面板数据模型的参数估计

本文把一般的回归分析中观测变量有测量误差的问题引入到面板数据模型中,考虑有测量误差的动态面板数据模型,推导出模型中参数满足的各种矩条件,利用广义矩估计的方法得到了模型中参数的一致估计。并且给出了模型错误识别时参数的一个估计值。最后,对于不同的测量误差结构,通过Monte Carlo试验验证了我们给出三种估计的优良性。     

What difference do role models make? Investigating outcomes at a residential environmental education center

Role models may be essential in helping students develop environmental literacy and characteristics associated with positive youth development (PYD). We examine the identities of middle school students’ self-reported role models before, immediately after, and three months following an immersive 5-day residential environmental education (EE) experience that targets environmental responsibility, character development and leadership, and attitudes toward school as programmatic outcomes. We explore whether students who identified different types of role models score differently on these factors. Students who identified parents, teachers, and pastors as role models scored higher on each of these outcomes. Moreover, who students identified as role models changed significantly after the EE program. We discuss the potential influence of the program on role model development and implications for EE and PYD programs.     

Discrimination of rice panicles by hyperspectral reflectance data based on principal component analysis and support vector classification

Detection of crop health conditions plays an important role in making control strategies of crop disease and insect damage and gaining high-quality production at late growth stages. In this study, hyperspectral reflectance of rice panicles was measured at the visible and near-infrared regions. The panicles were divided into three groups according to health conditions: healthy panicles, empty panicles caused by Nilaparvata lugens Stul, and panicles infected with Ustilaginoidea virens. Low order derivative sp...