Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae （Helminthosporium oryzae Breda. de Hann）. Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 （the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy）, R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it＇s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Monitoring Flue-Cured Tobacco Leaf Chlorophyll Content under Different Light Qualities by Hyperspectral Reflectance

Rapid assessment of foliar chlorophyll content in tobacco is critical for assessment of growth and precise management to improve quality and yield while minimizing adverse environmental impact. Our objective is to develop a precise agricultural practice predicting tobacco-leaf chlorophyll-a content. Reflectance experiments have been conducted on flue-cured tobacco over 3 consecutive years under different light quality. Leaf hyperspectral reflectance and chlorophyll-a content data have been collected at 15-day intervals from 30 days after transplant until harvesting. We identified the central band that is sensitive to tobacco-leaf chlorophyll-a content and the optimum wavelength combinations for establishing new spectral indices (simple ratio index, RVI;normalized difference vegetation index, NDVI;and simple difference vegetation index, DVI). We then established linear and BackPropagation (BP) neural network models to estimate chlorophyll-a content. The central bands for leaf chlorophyll-a content are concentrated in the visible range (410 - 680 nm) in combination with the shortwave infrared range (1900 - 2400 nm). The optimum spectral range for the spectral band combinations RVI, NDVI, and DVI are 440 and 470 nm, 440 and 470 nm, and 440 and 460 nm, respectively. The linear RVI, NDVI, and DVI models, SMLR model and the BP neural network model have respective R2 values of 0.76, 0.77, 0.69, 0.78 and 0.86, and root mean square error values of 0.63, 1.60, 1.59, 2.04 and 0.05 mg chlorophyll-a/g (fresh weight), respectively. Our results identified chlorophyll-a sensitive spectral regions and new indices facilitate a rapid, non-destructive field estimation of leaf chlorophyll-a content for tobacco.

Nitrogen content diagnosis of apple trees canopies using hyperspectral reflectance combined with PLS variable extraction and extreme learning machine

Nitrogen(N)is an important mineral element in apple production.Rapid estimation of apple tree N status is helpful for achieving precise N management.The objective of this work was to explore partial least squares(PLS)regression in dimensional reduction of spectral data and build the diagnostic model.The spectral reflectance data were collected from Fuji apple trees with 4 levels of N fertilizer treatment in the Loess Plateau in 2018 and 2019 using an ASD portable spectroradiometer,and leaf total N content was obtained at the same time.The raw spectra were pretreated using Savitzky-Golay(SG)smoothing and a combination of SG and first-order derivative(SG_FD)or second-order derivative(SG_SD).The samples were divided into a calibration dataset and a prediction dataset using SPXY.Based on 4 factors of PLS regression,including latent variables(LVs),X-loading,variable importance in projection(VIP)and regression coefficients(RC),the 6 methods(LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02)were derived and used for variable extraction,based on which PLS model and ELM model were established.The results indicated that the spectral data processed by SG_FD had the highest signal-to-noise ratio and was selected for subsequent analysis.The amounts of variables extracted by LVs,X-loading,VIP_01,VIP_02,RC_01 and RC_02 were 6,11,18,305,26 and 88,respectively.The method of extracting variables with an RC threshold based on the minimum RMSEP(RC_02)could effectively avoid the omission of effective information.The RC_02 method was recommended for related research which required accurate wavelength information as a variable.The variable extraction method based on LVs generated an ELM model with a simple structure.The prediction results showed that the ELM model outperformed the PLS model.The PLS(LVs)_ELM model was the best;R2P,RMSEP and RPD were 0.837,2.393 and 2.220,respectively.

Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges

Many biochemical and physiological properties of plants that are of interest to breeders and geneticists have extremely low throughput and/or can only be measured destructively.This has limited the use of information on natural variation in nutrient and metabolite abundance,as well as photosynthetic capacity in quantitative genetic contexts where it is necessary to collect data from hundreds or thousands of plants.A number of recent studies have demonstrated the potential to estimate many of these traits from hyperspectral reflectance data,primarily in ecophysiological contexts.Here,we summarize recent advances in the use of hyperspectral reflectance data for plant phenotyping,and discuss both the potential benefits and remaining challenges to its application in plant genetics contexts.The performances of previously published models in estimating six traits fromhyperspectral reflectance data in maizewere evaluated on newsample datasets,and the resulting predicted trait values shown to be heritable(e.g.,explained by genetic factors)were estimated.The adoption of hyperspectral reflectance-based phenotyping beyond its current uses may accelerate the study of genes controlling natural variation in biochemical and physiological traits.

Estimation of citrus canker lesion size using hyperspectral reflectance imaging

The Citrus industry has need for effective approaches to remove fruit with canker before they are shipped to selective international market such as the European Union.This research aims to determine the detectable size limit for cankerous lesions using hyperspectral imaging approaches.Previously developed multispectral algorithms using visible to near-infrared wavelengths,were used to segregate cankerous citrus fruits from other peel conditions(normal,greasy spot,insect damage,melanose,scab and wind scar).However,this previous work did not consider lesion size.A two-band ratio method with a simple threshold based classifier(ratio of reflectance at wavelengths 834 nm and 729 nm),which gave maximum overall classification accuracy of 95.7%,was selected for lesion size estimation in this study.The smallest size of cankerous lesion detected in terms of equivalent diameter was 1.66 mm.The effect of variation of threshold values and number of erosion cycles(applying morphological erosion multiple times to the image)on estimation of smallest detectable lesion was observed.It was found that small threshold values gave better canker classification accuracies,while exhibiting a lower overall classification accuracy.Meanwhile,higher threshold values portrayed the opposite tendency.The threshold value of 1.275 gave the optimum tradeoff between canker classification accuracy,overall classification accuracy and minimal lesion size detection.Increasing the number of erosion cycles reduced detection rates of smaller canker lesions,leading to the conclusion that a single erosion cycle gave the best size estimation results.The erosion kernel of the size 3 mm×3 mm was used during the exploration.

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 SD r, SD b and their integration.

Evaluation of atmospheric corrections on hyperspectral data with special reference to mineral mapping

Hyperspectral images have wide applications in the fields of geology,mineral exploration,agriculture,forestry and environmental studies etc.due to their narrow band width with numerous channels.However,these images commonly suffer from atmospheric effects,thereby limiting their use.In such a situation,atmospheric correction becomes a necessary pre-requisite for any further processing and accurate interpretation of spectra of different surface materials/objects.In the present study,two very advance atmospheric approaches i.e.QUAC and FLAASH have been applied on the hyperspectral remote sensing imagery.The spectra of vegetation,man-made structure and different minerals from the Gadag area of Karnataka,were extracted from the raw image and also from the QUAC and FLAASH corrected images.These spectra were compared among themselves and also with the existing USGS and JHU spectral library.FLAASH is rigorous atmospheric algorithm and requires various parameters to perform but it has capability to compensate the effects of atmospheric absorption.These absorption curves in any spectra play an important role in identification of the compositions.Therefore,the presence of unwanted absorption features can lead to wrong interpretation and identification of mineral composition.FLAASH also has an advantage of spectral polishing which provides smooth spectral curves which helps in accurate identification of composition of minerals.Therefore,this study recommends that FLAASH is better than QUAC for atmospheric correction and correct interpretation and identification of composition of any object or minerals.

Study on Hyperspectral Estimation Model of Chlorophyll Content in Grape Leaves

In view of the shortage of using traditional methods to monitor chlorophyll content, hyperspectral technology was used to estimate the chlorophyll content of grape leaves rapidly, accurately and non-destructively. Based on the data of hyperspectral reflectivity and SPAD value of grape leaves collected from Wanjishan grape planting base in Tai an, the correlations of SPAD value with the original spectral reflectivity of grape leaves and its first derivative were analyzed to select sensitive bands, and an estimation model of chlorophyll content in grape leaves based on hyperspectral reflectivity was established. The best model was SPAD = 59.352＋ 44 836.313 R 601 .

A Spectral Index for Estimating Soil Salinity in the Yellow River Delta Region of China Using EO-1 Hyperion Data

Soil salinization is one of the most common land degradation processes. In this study, spectral measurements of saline soil samples collected from the Yellow River Delta region of China were conducted in laboratory and hyperspectral data were acquired from an EO-1 Hyperion sensor to quantitatively map soil salinity in the region. A soil salinity spectral index (SSI) was constructed from continuum-removed reflectance (CR-reflectance) at 2052 and 2203 nm, to analyze the spectral absorption features of the salt-affected soils. There existed a strong correlation (r = 0.91) between the SSI and soil salt content (SSC). Then, a model for estimation of SSC with SSI was established using univariate regression and validation of the model yielded a root mean square error (RMSE) of 0.986 and an R2 of 0.873. The model was applied to a Hyperion reflectance image on a pixel-by-pixel basis and the resulting quantitative salinity map was validated successfully with RMSE = 1.921 and R2 = 0.627. These suggested that the satellite hyperspectral data had the potential for predicting SSC in a large area.

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 rim. They were followed by 444, 524, 576, 594, 804, 849, 974, 1074, 1219, 1510, and 2194 rim. 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.

Analysis of vegetation indices derived from aerial multispectral and ground hyperspectral data

Aerial multispectral images are a good source of crop,soil,and ground coverage information.Spectral reflectance indices provide a useful tool for monitoring crop growing status.A series of aerial images were obtained by an airborne MS4100 multispectral imaging system on the cotton and soybean field.Ground hyperspectral data were acquired with a ground-based integration system at the same time.The Normalized Difference Vegetative Index(NDVI),Simple Ratio(SR),and Soil Adjusted Vegetation Index(SAVI)calculated from both systems were analyzed and compared.The information derived from aerial multispectral images has shown the potential to monitor the general growth status of crop field.The vegetation indices derived from both systems were significantly different(p-value was 0.073 atα=0.1 level)at the early growing stage of crops.The correlation coefficients of the image NDVI and ground NDVI were 0.3029 for soybean field and 0.338 for cotton field.SAVI and SR were not correlated.

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(mSR705L1-mSR705L3) 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 mSR705L1 and mSR705L3 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 mSR705L1-mSR705L3 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(mSR705L1-mSR705L3) greatly reduced the influence of the stages and genotypes in assessing the N status with re-flectance 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.