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.

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.

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.

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 .

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.