Nondestructive estimation of bok choy nitrogen status with an active canopy sensor in comparison to a chlorophyll meter

Precise estimation of vegetable nitrogen(N)status is critical in optimizing N fertilization management.However,nondestructive and accurate N diagnostic methods for vegetables are relatively scarce.In our two-year field experiment,we evaluated whether an active canopy sensor(GreenSeeker)could be used to nondestructively predict N status of bok choy(Brassica rapa subsp.chinensis)compared with a chlorophyll meter.Results showed that the normalized difference vegetation index(NDVI)and ratio vegetation index(RVI)generated by the active canopy sensor were well correlated with the aboveground biomass(AGB)(r=0.698–0.967),plant N uptake(PNU)(r=0.642–0.951),and root to shoot ratio(RTS)(r=-0.426 to-0.845).Compared with the chlorophyll meter,the active canopy sensor displayed much higher accuracy(5.0%–177.4%higher)in predicting AGB and PNU and equal or slightly worse(0.54–1.82 times that of the chlorophyll meter)for RTS.The sensor-based NDVI model performed equally well in estimating AGB(R2=0.63)and PNU(R2=0.61),but the meter-based model predicted RTS better(R2=0.50).Inclusion of the days after transplanting(DAT)significantly improved the accuracy of sensor-based AGB(19.0%–56.7%higher)and PNU(24.6%–84.6%higher)estimation models.These findings suggest that the active canopy sensor has a great potential for nondestructively estimating N status of bok choy accurately and thus for better N recommendations,especially with inclusion of DAT,and could be applied to more vegetables with some verification.

Rapid detection of chlorophyll content and distribution in citrus orchards based on low-altitude remote sensing and bio-sensors

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.