The nutritional status of citrus leaves is very important to the determining of fertilization plans.The spectrum technique is a quick,un-injured method and is becoming widely used for plant nutrient estimation.The pos...The nutritional status of citrus leaves is very important to the determining of fertilization plans.The spectrum technique is a quick,un-injured method and is becoming widely used for plant nutrient estimation.The possibility and method of using spectrum technique to estimate the nutrient of citrus leaf was explored in this study.A total amount of 135 leaves from the mature spring shoots of navel orange trees(C.sinensis Osbeck,“Newhall”)were collected and randomly grouped into two sets of samples:100 leaves for the calibration set and 35 leaves for the prediction set.The hyperspectral images were scanned upper and lower side of each leaf and then the total nitrogen(N)and phosphorus(P)contents of each leaf were measured.The raw spectra data were extracted to generate average spectra curves,preprocessed with five different methods,and was used to build N and P content prediction models.The performances of the five preprocessing methods,i.e.,Savitzky-Golay smoothing(SGS),standard normal variate(SNV),multiplicative scatter correction(MSC),first-derivative(1-Der)and second-derivative(2-Der),were tested with linear partial least squares(PLS)models and nonlinear least squares-support vector machine(LS-SVM)models.The results showed that the SG-PLS and PLS were the best for the N predicting(Rp=0.9049,RMSEP=0.1041)and P(Rp=0.9235,RMSEP=0.0514)in citrus leaves,respectively;the hyperspectral image data from leaf‟s upper side predicting better for the contents of N and P.The study suggested that the hyperspectral image data from the upper side of the citrus leaves are suitable for nondestructive estimation of nutrient content.展开更多
The study aimed to investigate the fast and nondestructive method for detecting carbon and nitrogen content in citrus canopy.The multispectral imagery of Tarocco blood orange(Citrus sinensis L.Osbeck)plant canopy was ...The study aimed to investigate the fast and nondestructive method for detecting carbon and nitrogen content in citrus canopy.The multispectral imagery of Tarocco blood orange(Citrus sinensis L.Osbeck)plant canopy was obtained by a multispectral camera array mounted at an eight-rotor unmanned aerial vehicle(UAV)flying at an altitude of 100 m above the canopy in Wanzhou District of Chongqing Municipality,China.Average spectral reflectance data of the whole canopy,mature leaf areas and young leaves areas were extracted from the imagery.Two spectral pre-processing methods,multiplicative scatter correction(MSC)and standard normal variable(SNV),and two modeling methods,the partial least squares(PLS)and the least squares support vector machine(LS-SVM),were adopted and compared for their prediction accuracy of total content of nitrogen,soluble sugar and starch in the leaves.The results showed that,based on the spectral data extracted from the mature leaves in the multispectral imagery,the PLS model based on the original spectrum obtained a Rp(correlation coefficient)of 0.6469 and RMSEP(root mean squares error of prediction)of 0.1296,suggested that it was the best for the prediction of total nitrogen content;the PLS model based on MSC(multiplicative scatter correction)spectrum pre-processing was the best for predicting total soluble sugar content(Rp=0.6398 and RMSEP=8.8891);and the LS-SVM model based on MSC was the best for the starch content prediction(Rp=0.6822 and RMSEP=14.9303).The prediction accuracy for carbon and nitrogen contents based on the spectral data extracted from the whole canopy and the young leaves were lower than that from the mature leaves.The results indicate that it is feasible to estimate the carbon and nitrogen contents by low-altitude airborne multispectral images.展开更多
基金the financial support from the International Science&Technology Cooperation program of China(2013DFA11470)International Science&Technology Cooperation Program of Chongqing(CSTC2011gjhz80001)+2 种基金National High Technology Research&Development Program of China(2012AA101904)Chongqing Science&Technology Project(cstc2012gg-yyis80002)Chongqing Science&Technology Support&Demonstration Project(CSTC2014fazktpt80015&CSTC2014fazktjcsf80031).
文摘The nutritional status of citrus leaves is very important to the determining of fertilization plans.The spectrum technique is a quick,un-injured method and is becoming widely used for plant nutrient estimation.The possibility and method of using spectrum technique to estimate the nutrient of citrus leaf was explored in this study.A total amount of 135 leaves from the mature spring shoots of navel orange trees(C.sinensis Osbeck,“Newhall”)were collected and randomly grouped into two sets of samples:100 leaves for the calibration set and 35 leaves for the prediction set.The hyperspectral images were scanned upper and lower side of each leaf and then the total nitrogen(N)and phosphorus(P)contents of each leaf were measured.The raw spectra data were extracted to generate average spectra curves,preprocessed with five different methods,and was used to build N and P content prediction models.The performances of the five preprocessing methods,i.e.,Savitzky-Golay smoothing(SGS),standard normal variate(SNV),multiplicative scatter correction(MSC),first-derivative(1-Der)and second-derivative(2-Der),were tested with linear partial least squares(PLS)models and nonlinear least squares-support vector machine(LS-SVM)models.The results showed that the SG-PLS and PLS were the best for the N predicting(Rp=0.9049,RMSEP=0.1041)and P(Rp=0.9235,RMSEP=0.0514)in citrus leaves,respectively;the hyperspectral image data from leaf‟s upper side predicting better for the contents of N and P.The study suggested that the hyperspectral image data from the upper side of the citrus leaves are suitable for nondestructive estimation of nutrient content.
基金the International Science&Technology Cooperation Program of China(2013DFA11470)National Science&Technology Pillar Program(2014BAD16B0103)+2 种基金Chongqing Science&Technology support demonstration project(cstc2014fazktpt80015)Jiangxi Province 2011 Collaborative Innovation Special Funds“Co-Innovation Center of the South China Mountain Orchard Intelligent Management Technology and Equipment”(Jiangxi Finance Refers to[2014]NO 156)Chongqing Key Laboratory of Citrus(CKLC201302).
文摘The study aimed to investigate the fast and nondestructive method for detecting carbon and nitrogen content in citrus canopy.The multispectral imagery of Tarocco blood orange(Citrus sinensis L.Osbeck)plant canopy was obtained by a multispectral camera array mounted at an eight-rotor unmanned aerial vehicle(UAV)flying at an altitude of 100 m above the canopy in Wanzhou District of Chongqing Municipality,China.Average spectral reflectance data of the whole canopy,mature leaf areas and young leaves areas were extracted from the imagery.Two spectral pre-processing methods,multiplicative scatter correction(MSC)and standard normal variable(SNV),and two modeling methods,the partial least squares(PLS)and the least squares support vector machine(LS-SVM),were adopted and compared for their prediction accuracy of total content of nitrogen,soluble sugar and starch in the leaves.The results showed that,based on the spectral data extracted from the mature leaves in the multispectral imagery,the PLS model based on the original spectrum obtained a Rp(correlation coefficient)of 0.6469 and RMSEP(root mean squares error of prediction)of 0.1296,suggested that it was the best for the prediction of total nitrogen content;the PLS model based on MSC(multiplicative scatter correction)spectrum pre-processing was the best for predicting total soluble sugar content(Rp=0.6398 and RMSEP=8.8891);and the LS-SVM model based on MSC was the best for the starch content prediction(Rp=0.6822 and RMSEP=14.9303).The prediction accuracy for carbon and nitrogen contents based on the spectral data extracted from the whole canopy and the young leaves were lower than that from the mature leaves.The results indicate that it is feasible to estimate the carbon and nitrogen contents by low-altitude airborne multispectral images.
