Study On Wheat Seedling Monitoring Based On Hyperspectral Imaging Technology

Wheat was the world's most widely distributed crops. It is also one of the largest food cropacreage and among the largest food production in China. It was an important commodity grainand strategic reserves of staple food varieties. Seedling growth and development affectes thewheat's final yield, quality and safety. So seedling vigor agronomists, breeders, biochemists, areconcerning about the crop characteristics. Chlorophyll content as an indicator of the cropphotosynthesis ability and the growing status. Currently, the monitoring of crop growing statuswere mostly focused on the middle and final growth period, where spectroscopy analysis iswidely used to capture group parameters. Seedling monitoring is focused more on the analysisof individual parameter; however it was lack of effective detection method. Hyperspectralimaging, known also as spectroscopic imaging, is an emerging technique that integratesconventional imaging and spectroscopy to attain both spatial and spectral information from anobject. It could obtain the spectrum of each local area, and has great advantage on differentiatingindividual differences. It has been used in aviation and satellite remote sensing for years.Recently, with the reducing of cost, the hyperspectral imager for field purpose is possible. Forthis study, we monitor the wheat seedling growth by hyperspectral imaging technology and highresolution image integration. In this paper, we used the hyperspectral imagers to detect thechlorophyll content for salt stress of the wheat seedling, freezing injury of wheat seedlings underlow-temperature stress, drought injury of wheat seedlings under low-water stress, recognition ofWheat Pre-harvest Sprouting, and the chlorophyll content of wheat seedings.1. An auto-development pushbroom imaging spectrometer (PIS) with wavelength range of400-1000nm was applied to measure the detection of chlorophyll content for salt stress of thewheat seedling by hyperspectral imaging. It showed that according to images of spectralimaging for leaves of Chinese Spring (Salt-sensitive), Zhouyuan9369(common and high-yield)and Changwu134(salt-tolerant) wheat seedling under salt stress, growth of salt-sensitiveChinese Spring wheat seedling was inhibited and it was feasible to carry out qualitativeanalysis.We collected the imaging spectrum of126wheat samples in the wavelength range of400~1000nm, selected the average spectrum, exerted Correlation Analysis on the spectrum ofwheat seedlings with the SPAD value, It could be seen that the biggest absolute value of thecorrelation coefficient was at693nm, which was considered as the characteristics wavelength ofwheat seedlings. To establish the linear regression model using this wavelength, and substitutedthe reflectance data of each point into the model, then we got the SPAD value of each point, toform the relative content distribution map of chlorophyll, whereby to diagnose the distribution of seedlings component. Partial least square regression (PLSR) method was used to build thecalibration model. Results showed that the extracted hyperspectral spectra had high correlationwith chlorophyll content. The correlation coefficient of the calibration model is R=0.8138, thestandard error of prediction is SEP=4.75.2. Diagnosis of freezing stress in wheat seedlings using hyperspectral imaging technology.Imaging spectrometry and image integration were used to study the extent of freezing injury onwheat seedlings on three dates in2010. Thirty wheat cultivars with limited freezing resistancethat are usually grown in Southern China were grown in pots located outdoors in Beijing.Imaging spectra of potted wheat samples were acquired in the spectral range of450nm to900nm, and a polyphenol tester was used to determine the nitrogen balance index of wheat samples.Increased freezing injury was related to increased spectral reflectance in the450nm to650nmwave band and decreased spectral reflectance in the700nm to900nm wave band. Averagespectral reflectance of wheat seedling canopies was negatively correlated (-0.7) with nitrogenbalance index in the red edge area between650nm and700nm. Absolute values of correlationcoefficients under freezing stress at three measuring dates reached a maximum at680nm, andthis wavelength was used as the characteristic wavelength for freezing-injury diagnosis of thewheat seedlings. From spectral images at this characteristic wavelength, it was feasible tointuitively observe the area and extent of freezing injured wheat seedlings. Our results show thatit is feasible to monitor freezing stress of wheat seedlings by use of hyperspectral imaging whichcould accurately reflect freezing-injured parts of wheat seedlings.3. Diagnosis of drought stress in wheat seedlings using hyperspectral imaging technology.The severity of the drought stress conditions different period of three acquisition imagingspectrometer data2011-11-15,2011-11-23,2011-12-01wheat seedlings, respectively, andrecord the corresponding seedling per plant leaf chlorophyll SPAD values, of wheat seedling as2011-11-23periods of drought stress aggravated Yangmai15Yangmai158, the the SPADaverage of Jingdong8monoclonal measured three poor drought resistance of potted wheatleaves begin to reduce small, and better drought resistance Nongda211measured SPAD valueof leaves of wheat seedlings monoclonal close to the measured values in the two periods isinsignificant. Analysis found that hyperspectral imaging technology on Wheat canopy spectracollected under different drought stress period in the wavelength range of400~1000nm,450nm~900nm spectral wavelength range2012-12-01wheat seedlings during drought stress isthe most serious wheat seedling, mean canopy spectral reflectance, and do not have thecharacteristic peaks of the spectral reflectance of green vegetation. Subsequently, seedlingdrought stress under the canopy spectral reflectance, first derivative, and the chlorophyll SPAD average of measured correlation coefficient curve, correlation analysis, we found that thespectral curve in the range of700~780nm red band within a positive effect related to asignificant, suggesting that drought stress had a greater impact may be the curve red edge, theother band area, significant correlation coefficients are very low. We can see from the abovefindings: Imaging Spectrometer PIS has the advantages of imaging and it maps thecharacteristics of unity, to extract the spectral characteristics, and to reflect the growingconditions in the wheat seedling drought stress.4. The application of hyperspectral imaging acquisition field of wheat seedling canopy andindividual plant leaf spectral imaging, based on genetic algorithm selected six characteristicbands corresponding to the characteristic wavelength, the leaves of wheat seedlings per plantwere measured nitrogennutritional component information the amount of chlorophyll tworepresentative wheat growing good or bad, true value, to build mathematical models usingpartial least squares (PIS), correlation analysis, the wheat seedlings per plant leaf componentinformationchoice of different spectral wavelength range of model results, the results show: thecombination of partial least squares (PIS) and the blades of wheat seedlings monoclonalnitrogen, chlorophyll value of the model based on genetic algorithm, are superior based on thefull spectrum of wavelengthsthe range of FS-PLS and ES-PLS model based on experience toselect the wavelength range. Comprehensive evaluation to predict wheat seedling nitrogencomponent information modeling results the correlation coefficient R=0.9, correction standarddeviation of the SEC=0.0841, standard error of prediction of the RPD=1.4358, the relativepercentile RSEP=9.3893%. At the same time, the leaf chlorophyll value of the wheat seedlingper plant based on genetic algorithm combined with partial least squares method to establish themodel results of the correlation coefficient R=0.8303, corrected standard deviation of the SEC=0.1169, standard error of prediction the RPD=0.8064, and the relative percentage value RSEP=13.899%.5. Study on recognition of wheat pre-harvest sprouting based on hyperspectral imagingtechnology. Imaging spectrometer was used to analyze120strains of wheatears under fourdifferent watering treatment ways. Comparative analysis was carried out for mean spectralreflectivity of ears and seeds of the four groups of wheat wheatear, and results showed thatextracted RGB color effect image based on characteristic wavelength of hyperspectral imagingwas better than imaging image of wheat pre-harvest sprouting based on machine vision.Whether wheat pre-harvest sprouting occurred could be reflected by spectral characteristics.Especially at675nm, there was a significant difference for spectral reflectivity betweensprouting wheat and non-sprouting wheat. Sprouting parts of wheatears presented absorption valley at675nm, while non-sprouting parts didn't present it. Therefore, it was feasible to judgewheatears' sprouting situations according to changes of spectral curve at675nm. Therefore, it isillustrated that imaging spectra can better differentiate wheat pre-harvest sprouting situationsunder different watering treatment ways, suggesting that it is completely feasible to usehyperspectral imaging technology to carry out nondestructive monitoring for wheat pre-harvestsproutingThe results above of the all, indicated that hyperspectral imaging were suitable for thenon-invasive detection of chlorophyll content of wheat seedling.