Research On Extraction Of The Information Of Physical And Chemical Properties Of Fruit Trees Based On Spectral Reflectance Data

The paper takes fruit trees as the research object, bases on reflectance spectroscopy of canopy or single leaf of fruit trees, exploratory research on Extraction of the information of Physical and chemical properties of fruit trees(the species of Fruit trees; the characteristics of the spectral reflectance of fuji apple trees which are intimidated by disease or insect pest; the characteristics of the spectral reflectance of fruit trees blossoms suffered frost with different degrees; the relation between the fruit quantity and the spectral reflectance of fuji apple tree; the three major nutrients〈TN, TP, TK〉content, 4 kinds of trace elements〈Fe, Mn, Cu, Zn〉content),the study attempts to explore the effective ways and means to monitor or identify the information of physical and chemical properties of fruit trees.The main research content and conclusions are as follows:1. Using the spectral reflectance data (R_λ) of canopies, the paper identifis seven species of fruit trees during flowering period. Firstly, it compares the identification capacity of six kinds of satellite sensors and four kinds of vegetation index on the basis of resampling the spectral data with Six kinds of pre-defined filter function and calculating vegetation index. Then, it structures a BP neural network model for identifying seven species of fruit trees on the basis of choosing the best transformation of R_λand optimizing the model parameters.The main conclusions are:(1) The order of the identification capacity of Six kinds of satellite sensors from power to weak are : MODIS,ETM+,QUICKBIRD,IKONOS,HRG,ASTER; (2) Among four kinds of vegetation index, the identification capacity of RVI is the most powerful, next is NDVI, the identification capacity of SAVI or DVI is relatively weak; (3) The identification capacity of RVI and NDVI that are calculated with the reflectances of near-infrared and blue channels of ETM + or MODIS sensor are relatively powerful;(4) Among R_λand it`s 22 kinds of transformation data, d1[㏒(1/R_λ)]( derivative gap is set 9 nm) is the best transformation for structuring BP neural network model.2. Using the spectral reflectance data (R_λ) of canopies, the paper identifies seven species of fruit trees bearing fruit in the fruit mature period. Firstly, it compares the fruit tree species identification capability of six kinds of satellite sensors and four kinds of vegetation index through re-sampling the spectral data with six kinds of pre-defined filter function and the related data processing of calculating vegetation indexes. Then, it structures a BP neural network model for identifying seven species of fruit trees on the basis of choosing the best transformation of R_λand optimizing the model parameters.The main conclusions are: (1) the order of the identification capability of six kinds of satellite sensors from strong to weak is : MODIS, ASTER, ETM +, HRG, QUICKBIRD, IKONOS; (2) among four kinds of vegetation indexes, the identification capability of RVI is the most powerful, the next is NDVI, the identification capability of SAVI or DVI is relatively weak; (3) The identification capability of RVI and NDVI those are calculated with the reflectance of near-infrared and red channels of ETM + or MODIS sensor are relatively powerful;(4) Among R_λand it's 22 kinds of transformation data, d1[㏒(1/R_λ)]( derivative gap is set 9 nm) is the best transformation for structuring BP neural network model; (5) The paper structures a 3-layer BP neural network model for identifying seven species of fruit trees using the best transformation of R_λwhich is d1[㏒(1/R_λ)]( derivative gap is set 9 nm).3. Basing on the spectral reflectance of three species of fruit trees which are in five different period of time, the study Analyses the problem of the best period of time to identify the species of fruit trees, the main conclusions are: the period of autumn is the best period of time to identify the species of fruit trees.4. The study attempts to explore the characteristics of the spectral reflectance of fruit trees blossoms suffered frost with different degrees, and try to evaluate quantitatively the levels of fruit trees blossoms suffered frost by spectra data . Firstly, we pretreat the spectra reflectance data of the blossoms of three species of fruit trees suffered frost with four levels , and analyze the spectra characteristics of the frosted fruit trees blossoms .Subsequently, we transform the spectra data by the first derivative with nine kinds of different wavelength intervals, and find out the special wavelength and Special wavelength range, and Select three groups of specific derivative Spectra in the transformation results. Finally, we build the quantitative assessment models for the frosted blossoms of the three Species of fruit trees using the integral values which are calculated by the selected three groups of specific differential Spectra within the corresponding Special wavelength range respectively.The main conclusions are:(1) the reflectance spectroscopy of the blossoms suffered frost at each level of each species of the fruit trees emerges the lowest Valley area near 360 nm, and emerges the Scarp with the largest slope within the Wavelength range from 360 nm to 440 nm, the order of the slopes is: no suffered frost >suffered frost lightly >suffered frost moderate > suffered frost severe, the largest slopes of the four Scarps are all near 400 nm;(2) when the wavelength interval is set at 9 nm, the integral values that are calculated by the derivative spectra of the frosted blossoms at all levels of Crisp Pear, Shahong Peach, Fuji apple trees within the Wavelength range 396±20 nm, 400±20 nm, 410±20 nm are respectively the largest; (3) we establish respectively quantitative evaluation models based on the three groups of integral values which mentioned in (2).5. Yellow Leaves Disease and Red Mite Insect Pest of FuJi Apple Tree were used as Samples, the research attempts to explore the characteristics of the spectral reflectance of fuji apple trees which are intimidated by disease or insect pest, and try to evaluate quantitatively the degrees of disease or insect pest stress by spectra data.The main conclusions are: (1) The Spectral reflectance of each level of red mite insect pest within the wavelength range from 630 to 695 nm, R_severe>R_moderate>R_lightly>R_normal,the largest Coefficients of variation of the four Reflectances is at 684 nm, within the wavelength range from 730 to 950 nm, Rsevere < R_moderate < R_lightly normal,the largest Coefficients of variation of the four Reflectances is at 762 nm.the Spectral Reflectance of each level of yellow leaves disease within the wavelength range from 515 to 716 nm, R_severeR_moderate>R_lightly>R_normal,the largest Coefficients of variation of the four Reflectances is at 603 nm,within the wavelength range from 740 to 950 nm, Rsevere < R_moderate < R_lightly normal,the largest Coefficients of variation of the four Reflectances is at 764 nm. (2) The red border position of the Spectral Reflectance of fruit trees moves to shortwave with the level of disease or insect pest increasing.(3)The models that are used to evaluate quantitatively the degrees of disease or insect pest stress have higher precision .6. Red Fuji Apple Tree was used as object of the study, based on the spectra reflectance of apple tree which hanging on the quantity of fruit were not equal, The research attempts to explore the relation between the fruit quantity and the spectral reflectance of fuji apple tree, and try to evaluate quantitatively the fruit quantity with spectra data.The main conclusions are: (1) within the wavelength range from 560 to 673 nm, or from 760 to 950 nm, the difference of R_λof five levels of fruit quantity are large, the coefficient of variation of R_λof five levels of fruit quantity is larger when wavelength is 634 or 760 nm. the wavelength range nearby 592 or 655 or 696 nm, the difference of the first derivative spectral reflectance of five levels of fruit quantity is larger. (2) The red border position of the Spectral Reflectance of fruit trees moves to shortwave with the fruit quantity increasing.(3) the quantitive relation model between the optimal spectra index and area ratio of fruit is constructed.7.Red Fuji Apple Tree was used as object of study, the spectral reflectance (R_λ) of fresh leaves of fruit trees and the leaves total nitrogen (TN), total phosphorus (TP), total potassium (TK) contents were measured, and analyze the statistical correlation between the each element content and R_λas well as its several transformations (1/R_λ,lg(1/R_λ),d¹R_λ,d²R_λ,d¹[lg(1/R_λ)], d²[lg(1/R_λ)],lg(1/BNC),f′(R_λ),Dn) within the wavelength range from 400 nm to 900 nm by factor analysis method, and discover the spectral reflectance variant of the highest correlation coefficient . Subsequently, we carry on the regression analysis of each element content and the corresponding spectral reflectance variant of the highest correlation coefficient by stepwise regression method, and choose the eigenvalue wavelengths with which to carry on partial least squares regression modeling based on the least square error. The research is expected to evaluate the possibility and application potential of the method of spectral analysis on predicting the nutritional element of fruit trees.The results show : (1) The correlation coefficientis the highest between the leaves TN or TK content and the first derivative of spectral reflectance with derivative gap = 5 nm; the correlation coefficient is the highest between the leaves TP content and d¹[lg(1/R_λ)] with derivative gap = 9 nm; (2) The regression models that are established using the wavelengths selected by stepwise regression method when Sig(Tt′s a Probability numerical Higher than F detection value) is set to 0.02 and Ramoval is set to 0.03 have the better linear trend , and R2 values are higher than 0.8; (3) The method of spectral analysis have some applications potential to predict TN,TP,TK element of fruit trees .8. Red Fuji Apple Tree was used as object of study, the spectral reflectance (R_λ) of fresh leaves of fruit trees and the Fe,Mn,Cu,Zn element contents in the leaves were measured, and analyze the statistical correlation between the each element content and R_λas well as its several transformations (1/R_λ,lg(1/R_λ),d¹R_λ,d²R_λ,d¹[lg(1/R_λ)],d²[lg(1/R_λ)],lg(1/BNC),f′(R_λ),Dn)within the wavelength range from 400 nm to 900 nm by factor analysis method, and respectively find out the spectral reflectance Variant, which has the highest absolute value of the correlation coefficient with each of the element's content. Subsequently, we carry on the regression analysis to each element content and the corresponding spectral reflectance variant of the highest Correlation coefficient through the stepwise regression method, and choose the eigenvalue wavelengths with which to carry on partial least squares regression modeling based on the least error square sum. The research is expected to evaluate the possibility and application potential of the method of spectral analysis on predicting trace elements of fruit trees.The results show: (1) The correlation between the Fe, Mn, Cu, Zn elements content with R_λis rather weak and the correlation coefficient is the highest between the leaves Fe content and the first derivative of 4 points Difference of spectral reflectance f′(R_λ), or between the leaves Mn content and the first derivative of spectral reflectance with derivative gap = 17 nm d¹R_λ, or between the leaves Cu content and the first derivative of spectral reflectance with derivative gap = 25 nm d¹R_λ, or between the leaves Zn content and the first derivative of spectral reflectance with derivative gap = 15 nm d¹R_λ; (2) The result also show that the regression models that are established using the wavelengths selected when Sig(it′s a Probability numerical Higher than F detection value) is set to 0.01 and Ramoval is set to 0.02 have the better linear trend, and R2 values are higher than 0.8; (3) The method of spectral analysis have some applications potential to predict trace elements of fruit trees .