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The Remote Sensing Monitoring Of Apple Leaves Physiological And Biochemical Parameters And Nutrient

Posted on:2016-02-06Degree:MasterType:Thesis
Country:ChinaCandidate:X H ZhangFull Text:PDF
GTID:2283330461467501Subject:Cartography and Geographic Information System
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This study took apple grove an economic forest in northwest subhumid area as the research object. Through field observation, sampling and chemical analysis, analyzed original spectrum at different stages of the apple leaves, the changing rule of the biological and biochemical parameters and nutrient. This study established hyperspectral inversion models of chlorophyll SPAD value, moisture content, total nitrogen, total phosphorus and total potassium content of apple leaves, which could implement the real-time monitoring of physiological and biochemical parameters and apple nutrition diagnosis, provide the basis for the fruit trees fertilization.(1)The highest apple canopy original spectral reflectance is in the spectral bands of 350-675 nm, in the end flowering stage. The apple canopy original spectral reflectance is decreased from fruit-growth stage to the coloring of fruit stage, and increased in the harvest maturity stage. In the spectral bands of 675-1350 nm, the canopy original spectral reflectance is increased as the growth stage advanced from the end flowering stage to the coloring of fruit stage, and decreased in the harvest maturity stage. After entering the 1350 nm, the spectral reflectance sort from highest to lowest respectively are the end flowering stage, the coloring of fruit stage, fruitsetting stage, harvest maturity stage. In the spectral bands after 1950 nm, the end flowering stage’s spectral reflectance is higher than the other times.(2) With the increase of chlorophyll SPAD value, the red edge position caused red shift. The apple chlorophyll SPAD value and the total potassium content are increased from flowering stage to the coloring of fruit sage, and decreased in the harvest maturity stage. Leaf water content, total nitrogen and total phosphorus are reduced from the end flowering stage to the coloring of fruit stage, and increased in the harvest maturity stage.(3) The correlation analysis of apple leaves’ s original spectral reflectance, the first derivative spectral with chlorophyll SPAD value, water content of leaves, leaf total nitrogen, total phosphorus, total potassium content. It is negatively correlated in the visible region between the original spectrum and chlorophyll SPAD in different growth of the plant. The correlation coefficients of the original spectrum reflectance and leaf total nitrogen content across the whole band in two periods which are the end flowering stage and the fruiting stage. Leaf phosphorus content and the original spectral correlation coefficients across the whole band is the fruiting stage. Leaf potassium and original correlation coefficient spectra in the visible band of the period is the coloring of fruit Stage. Leaf potassium content and original correlation coefficients reached the significant levels in the end flowering stage and the fruiting stage. In the harvest maturity stage, the potassium content and original correlation coefficients reached the significant levels only in the red band 716-731 nm. The relevance of the leaf first derivative spectral and chlorophyll SPAD values, leaf water content, leaf total nitrogen, total phosphorus, total potassium content has increased.(4) Take a regression analysis between the apple leaf’s spectrum feature parameters and the chlorophyll SPAD value, water content of leaves, leaf total nitrogen, total phosphorus, total potassium content.The regression analysis is used in different growth stages in addition to the leaves water content. And make a regression analysis between the apple leaf’s vegetation index and the chlorophyll SPAD value, water content of leaves. Find the model fit well at different growth stages, and testing of the accuracy to carry out the estimation model which is better predictability and universality.
Keywords/Search Tags:Apple hyperspectral remote sensing, chlorophyll content, leaf water content, Nitrogen content, Phosphorus content, Potassium content
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