Remote measurement and diagnosis of the plants nutritional status is an important means for efficient easily and simple management system

and high-yield and high quality cultivation. So far

there is not yet much research on the nutrition diagnostic of fruit trees through low-altitude remote sensing data. We carried out the following experiments in order to provide a theoretical basis and technical support for the research and development of nutritional diagnosis technology of fruit trees based on low-altitude remote sensing data. In this work

the multi-spectral image information of ‘Hamlin ’orange plant canopies were obtained by a multi-spectral camera array mounted on the eight rotor Unmanned Aerial Vehicle（ UAV） at an altitude of 100 m above the canopyat 11: 00—13: 00 on a sunny day in spring. Then

the multi-spectral images were pre-processed by Pixel Wrench 2 of tetracam

average spectral reflectance of the whole canopy were individually extracted based on ENVI 4. 7. Twenty leaves from the mature spring shoots were collected from around crown of every tree. Total nitrogen

chlorophyll a

chlorophyll b and carotenoids contents of each plant were measured in the laboratory. The characteristic wavelengths were extracted by means of the correlation analysis of the average spectra of the plants with the nutrition content. A total amount of 88 citrus trees were collected and randomly grouped into two sets of samples: 66 plants for the calibration set and 22 plants for the prediction set. The two kinds of spectral pre-processing methods（ Multiplicative Scatter Correction（ MSC） and Standard Normal Variable（ SNV）） were adopted and four kinds of modeling methods（ Partial Least Squares（ PLS）

Multiple Linear Regression（ MLR）

Principal Component Regression（ PCR）and Least Squares Support Vector Machine（ LS-SVM）） were employed to estimate total nitrogen

chlorophyll a

chlorophyll b

and carotenoids content in canopy leaves. The results showed that the prediction accuracy of the MLR model based on SNV spectral pre-processing methods was the best for the prediction of total nitrogen

chlorophyll a and chlorophyll b content

correlation coefficients of prediction（ Rp） were 0. 8036

0. 8065

0. 8107

and Root Mean Square Error of Prediction（ RMSEP） were 0. 1363

0. 0427 and 0. 0243

respectively. The LS-SVM model based on SNV spectral pre-processing methods for the carotenoids content of crown was the best

which is with Rp= 0. 8535

RMSEP = 0. 0117. The results demonstrated that the airborne multi-spectral image information of citrus plants canopy could be used to estimate total nitrogen

chlorophyll a

chlorophyll b and carotenoids content in canopy leaves. This research results would provide a new way for accurate

efficient prediction of plants nutrition status of largescale citrus orchards.