Sensing crop nitrogen status with fluorescence indicators. A review

Tremblay, N.; Wang, Zhijie; Cerović, Zoran G.

doi:10.1007/s13593-011-0041-1

Cited by 222 publications

(229 citation statements)

References 119 publications

(169 reference statements)

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“…The fluorescence indices, SFR (index of chlorophyll content) and NBI (index of N sufficiency) measured with the Multiplex 3 sensor, were strongly related with crop canopy N density (CND) for most of the measurements made throughout the winter wheat growth stages. These results from this study are in agreement with recent reports which describe the potential of fluorescence sensing methods for the monitoring of crop N status of other crop species [58][59][60], indicating that fluorescence indices are also suitable to determine the N status and grain quality in winter wheat.…”

Section: Discussionsupporting

confidence: 93%

Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements

Song

Yang

et al. 2017

Remote Sensing

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Wheat grain protein content (GPC) is a key component when evaluating wheat nutrition.It is also important to determine wheat GPC before harvest for agricultural and food process enterprises in order to optimize the wheat grading process. Wheat GPC across a field is spatially variable due to the inherent variability of soil properties and position in the landscape. The objectives of this field study were: (i) to assess the spatial and temporal variability of wheat nitrogen (N) attributes related to the grain quality of winter wheat production through canopy fluorescence sensor measurements; and (ii) to examine the influence of spatial variability of soil N and moisture across different growth stages on the wheat grain quality. A geostatistical approach was used to analyze data collected from 110 georeferenced locations. In particular, Ordinary Kriging Analysis (OKA) was used to produce maps of wheat GPC, GPC yield, and wheat canopy fluorescence parameters, including simple florescence ratio and Nitrogen Balance Indices (NBI). Soil Nitrate-Nitrogen (NO 3 -N) content and soil Time Domain Reflectometry (TDR) value in the study field were also interpolated through the OKA method. The fluorescence parameter maps, soil NO 3 -N and soil TDR maps obtained from the OKA output were compared with the wheat GPC and GPC yield maps in order to assess their relationships. The results of this study indicate that the NBI spatial variability map in the late stage of wheat growth can be used to distinguish areas that produce higher GPC.

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Section: Discussionsupporting

confidence: 93%

Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements

Song

Yang

et al. 2017

Remote Sensing

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“…However, they mainly focused on using the far-red and red fluorescence ratios, fluorescence intensity as well as non-photochemical quenching etc. to detect the nutrients changes of crop (Tremblay et al 2011). The fluorescence spectra combined with multivariate analysis is still sparse at present.…”

mentioning

confidence: 99%

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice

Yang¹,

Gong²,

Shi³

et al. 2016

Plant Soil Environ.

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Paddy rice is one of the most important cereal crops in China. Nitrogen (N) is closely related to crops production by influencing the photosynthetic efficiency of paddy rice. In this study, laser-induced fluorescence (LIF) technology with the help of principal component analysis (PCA) and back-propagation neural network (BPNN) is proposed to monitor leaf N content (LNC) of paddy rice. The PCA is utilized to extract the characteristic variables of LIF spectra by analysing the major attributes. The results showed that the first three principal components (PCs) can explain 95.76% and 93.53% of the total variance contained in the fluorescence spectra for tillering stage and shooting stage, respectively. Then, BPNN was utilized to inverse the LNC on the basis of the first three PCs as input variables and can obtain the satisfactory inversion results (R 2 of tillering stage and shooting stage are 0.952 and 0.931, respectively; residual main range from -0.2 to 0.2 mg/g). The experimental results demonstrated that LIF technique combined with multivariate analysis will be a useful method for monitoring the LNC of paddy rice, which can provide consultations for the decision-making of peasants in their N fertilization strategies.

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“…Sensors have been developed using spectroradiometers, reflectometers, imagery from satellite sensors, and digital cameras to detect the N status of plants at the leaf and canopy level [39,40]. These methods have potential for measuring SNF under field conditions ( Table 1).…”

Section: Shoot Nitrogen Statusmentioning

confidence: 99%

Challenges in Using Precision Agriculture to Optimize Symbiotic Nitrogen Fixation in Legumes: Progress, Limitations, and Future Improvements Needed in Diagnostic Testing

Thilakarathna

Raizada

2018

Agronomy

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Precision agriculture (PA) has been used for ≥25 years to optimize inputs, maximize profit, and minimize negative environmental impacts. Legumes play an important role in cropping systems, by associating with rhizobia microbes that convert plant-unavailable atmospheric nitrogen into usable nitrogen through symbiotic nitrogen fixation (SNF). However, there can be field-level spatial variability for SNF activity, as well as underlying soil factors that influence SNF (e.g., macro/micronutrients, pH, and rhizobia). There is a need for PA tools that can diagnose spatial variability in SNF activity, as well as the relevant environmental factors that influence SNF. Little information is available in the literature concerning the potential of PA to diagnose/optimize SNF. Here, we critically analyze SNF/soil diagnostic methods that hold promise as PA tools in the short-medium term. We also review the challenges facing additional diagnostics currently used for research, and describe the innovations needed to move them forward as PA tools. Our analysis suggests that the nitrogen difference method, isotope methods, and proximal and remote sensing techniques hold promise for diagnosing field-level variability in SNF. With respect to soil diagnostics, soil sensors and remote sensing techniques for nitrogen, phosphorus, pH, and salinity have short-medium term potential to optimize legume SNF under field conditions.

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Sensing crop nitrogen status with fluorescence indicators. A review

Cited by 222 publications

References 119 publications

Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements

Spatial Variability Analysis of Within-Field Winter Wheat Nitrogen and Grain Quality Using Canopy Fluorescence Sensor Measurements

Estimation of nitrogen content based on fluorescence spectrum and principal component analysis in paddy rice

Challenges in Using Precision Agriculture to Optimize Symbiotic Nitrogen Fixation in Legumes: Progress, Limitations, and Future Improvements Needed in Diagnostic Testing

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