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| 1 | Effects of citrus tree-shape and spraying height of small unmanned aerial vehicle on droplet distribution显示文摘In order to explore the droplet penetration of spraying with unmanned aerial vehicle(UAV)on citrus trees with different shapes,the tests were carried out at different working heights.The material was five years old Cocktail grapefruit(Citrus paradisi cv.Cocktail)grafted on Trafoliata(Poncirus trifoliata L.Raf.)and the type of UAV sprayer used was the 3W-LWS-Q60S.A solution of 300 times Ponceau 2R diluents liquid instead of pesticide was used for citrus fields spraying and the droplets were collected by paper cards.Droplets deposition parameters were extracted and analyzed using digital image processing after scanning the cards.The results showed that:1)For the trees with round head shape canopy,the droplet depositions of the upper,middle and lower layers had a significant difference at 0.05 level.The droplet deposition had the best effect when the working height was 1.0 m,where the average droplet deposition densities were 39.97 droplets/cm2 and the average droplet size was 0.30 mm,but the droplet coverage(3.19%)was lower than that at the working height of 1.5 m(4.27%).2)Under three different working heights of UAV,the tree with open center shape can obtain higher droplet deposition density at all three layers than that with the round head shape canopy.It was especially prominent when the working height was 1.0 m,as the middle layer increased by 49.92%.However,the higher range of droplet deposition density meant larger fluctuation and dispersion.3)The open center shape canopy and the 1.0 m working height obviously improved the droplet coverage rate and droplet density in the citrus plant.For these parameters of open center shape citrus tree,there was no obvious difference in the front and rear direction,but in the left and middle part of the tree crown,the difference reached a 0.05 significant level.Considering droplet deposition characteristics and the spray uniformity,the UAV performed better when working on open center shape plants at a 1.0 m working height. | Zhang Pan Deng Lie Lyu Qiang He Shaolan Yi Shilai Liu Yande Yu Yongxu Pan Haiyang | 2016 | International Journal of Agricultural and Biological Engineering2016，9，4： | 37 |
| 2 | Prediction of nitrogen and phosphorus contents in citrus leaves based on hyperspectral imaging显示文摘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. | Liu Yanli Lyu Qiang He Shaolan Yi Shilai Liu Xuefeng Xie Rangjin Zheng Yongqiang Deng Lie | 2015 | International Journal of Agricultural and Biological Engineering2015，8，2： | 5 |
| 3 | Detection of Huanglongbing(citrus greening) based on hyperspectral image analysis and PCR显示文摘Huanglongbing (HLB, citrus greening) is one of the most serious quarantine diseases of citrus worldwide. To monitor in real-time, recognize diseased trees, and efficiently prevent and control HLB disease in citrus, it is necessary to develop a rapid diagnostic method to detect HLB infected plants without symptoms. This study used Newhall navel orange plants as the research subject, and collected normal color leaf samples and chlorotic leaf samples from a healthy orchard and an HLB-infected orchard, respectively. First, hyperspectral data of the upper and lower leaf surfaces were obtained, and then the polymerase chain reaction (PCR) was used to detect the HLB bacterium in each leaf. The PCR test results showed that all samples from the healthy orchard were negative, and a portion of the samples from the infected orchard were positive. According to these results, the leaf samples from the orchards were divided into disease-free leaves and HLB-positive leaves, and the least squares support vector machine recognition model was established based on the leaf hyperspectral reflectance. The effect on the model of the spectra obtained from the upper and lower leaf surfaces was investigated and different pretreatment methods were compared and analyzed. It was observed that the HLB recognition rate values of the calibration and validation sets based on upper leaf surface spectra under 9-point smoothing pretreatment were 100% and 92.5%, respectively. The recognition rate values based on lower leaf surface spectra under the second-order derivative pretreatment were also 100% and 92.5%, respectively. Both upper and lower leaf surface spectra were available for recognition of HLB-infected leaves, and the HLB PCR-positive leaves could be distinguished from the healthy by the hyperspectral modeling analysis. The results of this study show that early and nondestructive detection of HLBinfected leaves without symptoms is possible, which provides a basis for the hyperspectral diagnosis of citrus with HLB. | Kejian WANG Dongmei GUO Yao ZHANG Lie DENG Rangjin XIE Qiang LV Shilai YI Yongqiang ZHENG Yanyan MA Shaolan HE | 2019 | Frontiers of Agricultural Science and Engineering2019，6，2： | 3 |
| 4 | Estimation of carbon and nitrogen contents in citrus canopy by low-altitude remote sensing显示文摘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. | Liu Xuefeng Lyu Qiang He Shaolan Yi Shilai Hu Deyu Wang Zhitao Xie Rangjin Zheng Yongqiang Deng Lie | 2016 | International Journal of Agricultural and Biological Engineering2016，9，5： | 2 |
| 5 | Rootstocks influence fruit oleocellosis in ' Hamlin' sweet orange 显示文摘 | Yongqiang Zheng Lie Deng Shaolan He | 2012 | Scientia Horticulturae2012，128，2： | 1 |
| 6 | Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors显示文摘The accuracy of detecting the chlorophyll content in the canopy and leaves of citrus plants based on sensors with different scales and prediction models was investigated for the establishment of an easy and highly-efficient real-time nutrition diagnosis technology in citrus orchards.The fluorescent values of leaves and canopy based on the Multiplex 3.6 sensor,canopy hyperspectral reflectance data based on the FieldSpec4 radiometer and spectral reflectance based on low-altitude multispectral remote sensing were collected from leaves of Shatang mandarin and then analyzed.Additionally,the associations of the leaf SPAD(soil and plant analyzer development)value with the ratio vegetation index(RVI)and normalized differential vegetation index(NDVI)were analyzed.The leaf SPAD value predictive model was established by means of univariate and multiple linear regressions and the partial least squares method.Variable distribution maps of the relative canopy chlorophyll content based on spectral reflectance in the orchard were automatically created.The results showed that the correlations of the SPAD values obtained from the Multiplex 3.6 sensor,FieldSpec4 radiometer and low-altitude multispectral remote sensing were highly significant.The measures of goodness of fit of the predictive models were R^(2)=0.7063,RMSECV=3.7892,RE=5.96%,and RMSEP=3.7760 based on RVI_((570/800)) and R^(2)=0.7343,RMSECV=3.6535,RE=5.49%,and RMSEP=3.3578 based on NDVI[(570,800)(570,950)(700,840)].The technique to create spatial distribution maps of the relative canopy chlorophyll content in the orchard was established based on sensor information that directly reflected the chlorophyll content of the plants in different parts of the orchard,which in turn provides evidence for implementation of orchard productivity evaluation and precision in fertilization management. | Kejian Wang Wentao Li Lie Deng Qiang Lyu Yongqiang Zheng Shilai Yi Rangjin Xie Yanyan Ma Shaolan He | 2018 | International Journal of Agricultural and Biological Engineering2018，11，2： | 0 |
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