scholarly journals Canopy Fluorescence Sensing for in-Season Maize Nitrogen Status Diagnosis

2021 ◽  
Vol 13 (24) ◽  
pp. 5141
Author(s):  
Rui Dong ◽  
Yuxin Miao ◽  
Xinbing Wang ◽  
Fei Yuan ◽  
Krzysztof Kusnierek
Keyword(s):  
Linear Regression ◽  
Growth Stage ◽  
N Uptake ◽  
Bare Soil ◽  
Estimation Accuracy ◽  
Growth Stages ◽  
Fluorescence Sensing ◽  
N Nutrition ◽  
Regression Methods ◽  
N Status

Accurate assessment of crop nitrogen (N) status and understanding the N demand are considered essential in precision N management. Chlorophyll fluorescence is unsusceptible to confounding signals from underlying bare soil and is closely related to plant photosynthetic activity. Therefore, fluorescence sensing is considered a promising technology for monitoring crop N status, even at an early growth stage. The objectives of this study were to evaluate the potential of using Multiplex® 3, a proximal canopy fluorescence sensor, to detect N status variability and to quantitatively estimate N status indicators at four key growth stages of maize. The sensor measurements were performed at different growth stages, and three different regression methods were compared to estimate plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). The results indicated that the induced differences in maize plant N status were detectable as early as the V6 growth stage. The first method based on simple regression (SR) and the Multiplex sensor indices normalized by growing degree days (GDD) or N sufficiency index (NSI) achieved acceptable estimation accuracy (R2 = 0.73–0.87), showing a good potential of canopy fluorescence sensing for N status estimation. The second method using multiple linear regression (MLR), fluorescence indices and GDDs had the lowest modeling accuracy (R2 = 0.46–0.79). The third tested method used a non-linear regression approach in the form of random forest regression (RFR) based on multiple sensor indices and GDDs. This approach achieved the best estimation accuracy (R2 = 0.84–0.93) and the most accurate diagnostic result.

Proximal fluorescence sensing for in-season diagnosis of rice nitrogen status

2017 ◽  
Vol 8 (2) ◽  
pp. 343-348 ◽  
Author(s):  
S. Huang ◽  
Y. Miao ◽  
F. Yuan ◽  
Q. Cao ◽  
H. Ye ◽  
...  
Keyword(s):  
Stem Elongation ◽  
N Uptake ◽  
Growth Stages ◽  
Fluorescence Sensing ◽  
N Nutrition ◽  
N Concentration ◽  
Field Samples ◽  
Highly Correlated ◽  
Different Growth Stages ◽  
N Status

The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.

scholarly journals In-Season Diagnosis of Rice Nitrogen Status Using Proximal Fluorescence Canopy Sensor at Different Growth Stages

2019 ◽  
Vol 11 (16) ◽  
pp. 1847 ◽  
Author(s):  
Shanyu Huang ◽  
Yuxin Miao ◽  
Fei Yuan ◽  
Qiang Cao ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Field Experiments ◽  
Stem Elongation ◽  
N Uptake ◽  
Growth Stages ◽  
Cold Regions ◽  
N Nutrition ◽  
N Concentration ◽  
Field Samples ◽  
Different Growth Stages ◽  
N Status

Precision nitrogen (N) management requires an accurate and timely in-season assessment of crop N status. The proximal fluorescence sensor Multiplex®3 is a promising tool for monitoring crop N status. It performs a non-destructive estimation of plant chlorophyll, flavonol, and anthocyanin contents, which are related to plant N status. The objective of this study was to evaluate the potential of proximal fluorescence sensing for N status estimation at different growth stages for rice in cold regions. In 2012 and 2013, paddy rice field experiments with five N supply rates and two varieties were conducted in northeast China. Field samples and fluorescence data were collected in the leaf scale (LS), on-the-go (OG), and above the canopy (AC) modes using Multiplex®3 at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. The relationships between the Multiplex indices or normalized N sufficient indices (NSI) and five N status indicators (above-ground biomass (AGB), leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI)) were evaluated. Results showed that Multiplex measurements taken using the OG mode were more sensitive to rice N status than those made in the other two modes in this study. Most of the measured fluorescence indices, especially the N balance index (NBI), simple fluorescence ratios (SFR), blue–green to far-red fluorescence ratio (BRR_FRF), and flavonol (FLAV) were highly sensitive to N status. Strong relationships between these fluorescence indices and N indicators, especially the LNC, PNC, and NNI were revealed, with coefficients of determination (R2) ranging from 0.40 to 0.78. The N diagnostic results indicated that the normalized N sufficiency index based on NBI under red illumination (NBI_RNSI) and FLAV achieved the highest diagnostic accuracy rate (90%) at the SE and HE stages, respectively, while NBI_RNSI showed the highest diagnostic consistency across growth stages. The study concluded that the Multiplex sensor could be used to reliably estimate N nutritional status for rice in cold regions, especially for the estimation of LNC, PNC, and NNI. The normalized N sufficiency indices based on the Multiplex indices could further improve the accuracy of N nutrition diagnosis by reducing the influences of inter-annual variations and different varieties, as compared with the original Multiplex indices.

scholarly journals Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

2021 ◽  
Vol 13 (8) ◽  
pp. 1561
Author(s):  
Chinsu Lin ◽  
Siao-En Ma ◽  
Li-Ping Huang ◽  
Chung-I Chen ◽  
Pei-Ting Lin ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Sampling ◽  
Fuel Load ◽  
Estimation Accuracy ◽  
Slope Aspect ◽  
Stratified Random Sampling ◽  
Inventory Data ◽  
Fuel Loading ◽  
Regression Methods

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping.

scholarly journals Evaluation of Three Portable Optical Sensors for Non-Destructive Diagnosis of Nitrogen Status in Winter Wheat

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5579
Author(s):  
Jie Jiang ◽  
Cuicun Wang ◽  
Hui Wang ◽  
Zhaopeng Fu ◽  
Qiang Cao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Optical Sensors ◽  
Accurate Estimation ◽  
Growth Stages ◽  
N Accumulation ◽  
N Nutrition ◽  
N Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

The accurate estimation and timely diagnosis of crop nitrogen (N) status can facilitate in-season fertilizer management. In order to evaluate the performance of three leaf and canopy optical sensors in non-destructively diagnosing winter wheat N status, three experiments using seven wheat cultivars and multi-N-treatments (0–360 kg N ha−1) were conducted in the Jiangsu province of China from 2015 to 2018. Two leaf sensors (SPAD 502, Dualex 4 Scientific+) and one canopy sensor (RapidSCAN CS-45) were used to obtain leaf and canopy spectral data, respectively, during the main growth period. Five N indicators (leaf N concentration (LNC), leaf N accumulation (LNA), plant N concentration (PNC), plant N accumulation (PNA), and N nutrition index (NNI)) were measured synchronously. The relationships between the six sensor-based indices (leaf level: SPAD, Chl, Flav, NBI, canopy level: NDRE, NDVI) and five N parameters were established at each growth stages. The results showed that the Dualex-based NBI performed relatively well among four leaf-sensor indices, while NDRE of RS sensor achieved a best performance due to larger sampling area of canopy sensor for five N indicators estimation across different growth stages. The areal agreement of the NNI diagnosis models ranged from 0.54 to 0.71 for SPAD, 0.66 to 0.84 for NBI, and 0.72 to 0.86 for NDRE, and the kappa coefficient ranged from 0.30 to 0.52 for SPAD, 0.42 to 0.72 for NBI, and 0.53 to 0.75 for NDRE across all growth stages. Overall, these results reveal the potential of sensor-based diagnosis models for the rapid and non-destructive diagnosis of N status.

scholarly journals Understanding and managing nitrogen nutrition in grapevine: a review

OENO One ◽  
2021 ◽  
Vol 55 (1) ◽  
pp. 1-43
Author(s):  
Thibaut Verdenal ◽  
Ágnes Dienes-Nagy ◽  
Jorge E. Spangenberg ◽  
Vivian Zufferey ◽  
Jean-Laurent Spring ◽  
...  
Keyword(s):  
Management Practices ◽  
Nitrogen Nutrition ◽  
N Uptake ◽  
Production Costs ◽  
Integrative Approach ◽  
Past Century ◽  
Wine Quality ◽  
N Nutrition ◽  
N Metabolism ◽  
N Status

This review addresses the role of nitrogen (N) in vine balance and grape composition. It offers an integrative approach to managing grapevine N nutrition. Keeping in mind that N excess is just as detrimental to wine quality as N depletion, the control of grapevine N status, and ultimately must N composition, is critical for high-quality grape production. N fertilisation has been intensively used in the past century, despite plants absorbing only 30 to 40 % of applied N. By adapting plant material, soil management and vine balance to environmental conditions, it would be possible for grape growers to improve plant N use efficiency and minimise N input in the vineyard. Vineyard N management is a complex exercise involving a search for a balance between controlling vigour, optimising grape composition, regulating production costs and limiting pollution. The first part of this review describes grapevine N metabolism from root N uptake to vine development and grape ripening, including the formation of grape aroma compounds. The advantages and limits of methods available for measuring plant N status are addressed. The second part focuses on the parameters that influence grapevine N metabolism, distinguishing the impacts of environmental factors from those of vineyard management practices. Areas for further research are also identified.

scholarly journals Use of Fluorescence Sensing to Detect Nitrogen and Potassium Variability in Maize

2020 ◽  
Vol 12 (11) ◽  
pp. 1752 ◽  
Author(s):  
Rafael Siqueira ◽  
Louis Longchamps ◽  
Subash Dahal ◽  
Raj Khosla
Keyword(s):  
Growth Stage ◽  
N Uptake ◽  
Early Growth ◽  
Crop Growth ◽  
Fluorescence Sensor ◽  
Growth Stages ◽  
Crop Canopy ◽  
Maize Plants ◽  
Crop Growth Stage ◽  
Balance Index

Real-time fluoro-sensing is a promising crop sensing technology to support variable-rate nutrient management for precision agricultural practices. The objective of this study was to evaluate the potential of fluoro-sensing to detect the variability of nitrogen (N) and potassium (K) in the crop canopy at the early growth stages of maize (before the V6 crop growth stage). This study was conducted under greenhouse conditions in pots filled with silica sand, and maize plants were supplied with modified Hoagland’s solution with different rates of N and K. Sensor readings were collected using a Multiplex®3 fluorescence sensor and analyzed using ANOVA (analysis of variance) to test differences in crop response to nutrient rates. Regression analysis was used to assess the ability of fluorescence sensor-based indices to estimate N and K in the crop canopy. The results of this study indicate that all fluorescence indices under consideration enabled the detection of N variability in the maize canopy prior to the V2 crop growth stage. The NBI_B (nitrogen balance index blue) index enabled N uptake detection (R2 = 0.99) as early as the V2 crop growth stage. However, the fluorescence indices failed to identify K deficiency, as the maize plants with K treatments showed little to no variability of this nutrient at early crop growth stages as measured by plant tissue analysis. The results present a tremendous opportunity to assess N uptake at early growth stages of maize for precision nitrogen application. We recommend using fluorescence sensor-based NBI_B or NBI_R (Nitrogen balance index red) for early detection of nitrogen uptake in maize for precision nitrogen management.

Responses of Rice (Oryza sativa L.) Nitrogen Status Indicators to Nitrogen Rates during the Different Rice Growth Stages

2013 ◽  
Vol 726-731 ◽  
pp. 4411-4417 ◽  
Author(s):  
Qing Wen Zhang ◽  
Zheng Li Yang ◽  
Ai Ping Zhang ◽  
Ming Wang
Keyword(s):  
N Uptake ◽  
Nutrition Status ◽  
Growth Stages ◽  
N Availability ◽  
Regression Equations ◽  
N Application ◽  
N Nutrition ◽  
Application Rates ◽  
Significant Regression ◽  
Nitrogen Rates

The SPAD was shown as a diagnostic tool to assess the nitrogen (N) nutrition status. The objective of this study is to evaluate the performance of SPAD as N nutrition status for rice. We conducted two years field experiment in the Ningxia irrigation area. Five N application rates were applied to rice to obtain contrasting conditions of N availability. The leaves N concentrations, SPAD and N uptake by rice were assessed. The results showed that response of SPAD to N uptake rate depends on the developmental stage of the rice. The peak periods for N uptake by rice were the jointing-booting stage to the flowering stage. Significant regression equations were established between SPAD and N uptake. The SPAD meter was demonstrated to be a useful nondestructive system to aid in the evaluation of N nutrition status in rice. However, consistency in sample seasonal timing may necessitate to correlate SPAD values.

scholarly journals Use of fluorescence indices as predictors of crop N status and yield for greenhouse sweet pepper crops

2021 ◽  
Author(s):  
Romina de Souza ◽  
M. Teresa Peña-Fleitas ◽  
Rodney B. Thompson ◽  
Marisa Gallardo ◽  
Rafael Grasso ◽  
...  
Keyword(s):  
Optical Sensors ◽  
Management Practices ◽  
N Uptake ◽  
Relative Yield ◽  
Sweet Pepper ◽  
Growth Stages ◽  
N Content ◽  
Fluorescence Measurements ◽  
N Management ◽  
N Status

AbstractTo increase nitrogen (N) use efficiency and reduce water pollution from vegetable production, it is necessary to optimize N management. Fluorescence-based optical sensors are devices that can improve N fertilization through non-destructive field monitoring of crop variables. The aim of this work was to compare the performance of five fluorescence indices (SFR-R, SFR-G, FLAV, NBI-R, and NBI-G) to predict crop variables, as dry matter production, crop N content, crop N uptake, Nitrogen Nutrition Index (NNI), absolute and relative yield, in sweet pepper (Capsicum annuum) crops grown in greenhouse. Fluorescence measurements were periodically made with the Multiplex® 3.6 sensor throughout three cropping cycles subjected to five N application treatments. The performance of fluorescence indices to predict crop variables considered calibration and validation analyses. In general, the five fluorescence indices were strongly related with NNI, crop N content and relative yield. The best performing indices to predict crop N content and NNI at the early stages of the crops (i.e., vegetative and flowering phenological stages) were the SFR indices, both under red (SFR-R) and green (SFR-G) excitation. However, in the final stage of the crop (i.e., harvest stage), the best performing indices were NBI, both under red (NBI-R) and green (NBI-G) excitation, and FLAV. The two SFR indices best predicted relative yield of sweet pepper at early growth stages. Overall, the fluorescence sensor and the fluorescence indices evaluated were able to predict crop variables related to N status in sweet pepper. They have the capacity to be incorporated into best N management practices.

scholarly journals Estimating the Growth Indices and Nitrogen Status Based on Color Digital Image Analysis During Early Growth Period of Winter Wheat

2021 ◽  
Vol 12 ◽  
Author(s):  
Ben Zhao ◽  
Yonghui Zhang ◽  
Aiwang Duan ◽  
Zhandong Liu ◽  
Junfu Xiao ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Growth Period ◽  
Early Growth ◽  
Individual Growth ◽  
Growth Stages ◽  
Growth Indices ◽  
Indirect Methods ◽  
N Nutrition ◽  
Direct And Indirect Methods ◽  
N Status

The non-destructive estimation of plant nitrogen (N) status is imperative for timely and in-season crop N management. The objectives of this study were to use canopy cover (CC) to establish the empirical relations between plant growth indices [shoot dry matter (SDM), leaf area index (LAI), shoot N accumulation (SNA), shoot nitrogen concentration (SNC)], and CC as well as to test the feasibility of using CC to assess N nutrition index (NNI) from Feekes 3 to Feekes 6 stages of winter wheat. Four multi-locational (2 sites), multi-cultivars (four cultivars), and multi-N rates (0–300 kg N ha–1) field experiments were carried out during 2016 to 2018 seasons. The digital images of the canopy were captured by a digital camera from Feekes 3 to Feekes 6 stages of winter wheat, while SDM, LAI, SNA, and SNC were measured by destructive plant sampling. CC was calculated from digital images developed by self-programmed software. CC showed significant correlations with growth indices (SDM, LAI, and SNA) across the different cultivars and N treatments, except for SNC. However, the stability of these empirical models was affected by cultivar characteristics and N application rates. Plant N status of winter wheat was assessed using CC through two methods (direct and indirect methods). The direct and indirect methods failed to develop a unified linear regression to estimate NNI owing to the high dispersion of winter wheat SNC during its early growth stages. The relationships of CC with SDM, SNC and NNI developed at individual growth stages of winter wheat using both methods were highly significant. The relationships developed at individual growth stages did not need to consider the effect of N dilution process, yet their stability is influenced by cultivar characteristics. This study revealed that CC has larger limitation to be used as a proxy to manage the crop growth and N nutrition during the early growth period of winter wheat despite it is an easily measured index.

Expression of nitrogen transporter genes in roots of winter wheat (Triticum aestivum L.) in response to soil drought with contrasting nitrogen supplies

2016 ◽  
Vol 67 (2) ◽  
pp. 128 ◽  
Author(s):  
Jianfeng Duan ◽  
Hui Tian ◽  
Yajun Gao
Keyword(s):  
Drought Stress ◽  
Winter Wheat ◽  
Growth Stage ◽  
N Uptake ◽  
Triticum Aestivum L ◽  
Ammonium Transporter ◽  
Soil Drought ◽  
Growth Stages ◽  
N Application ◽  
High Affinity

The expression of nitrate and ammonium transporter genes in the roots of winter wheat in response to drought stress is largely unknown. A greenhouse experiment was established to study the expression of five putative nitrate transporter (NRT) genes (TaNRT2.1, TaNRT2.2, TaNRT2.3, TaNRT1.1, TaNRT1.2) and three ammonium transporter (AMT) genes (TaAMT1.1, TaAMT1.2, TaAMT2.1) in the roots of winter wheat in response to soil drought under conditions of limited nitrogen (N) (no N added) and adequate N (addition of 0.3 g N kg–1 soil). Two wheat genotypes with low and high N-uptake efficiencies were used, and water-stress treatments were applied at the vegetative and reproductive growth stages of wheat. Expression of all of the genes was quantified using real-time reverse transcription PCR. The results indicated that wheat plants growing in the N-adequate soil were more sensitive to drought stress than those growing in the N-limited soil. The response of the expression of the NRT and AMT genes to soil drought largely depends on N application, wheat genotype and growth stage. The expression of the two low-affinity NRT genes (i.e. TaNRT1.1 and TaNRT1.2) in the N-inefficient genotype XY6 was mainly induced by drought stress, but the expression of the two genes in the N-efficient genotype XY107 was repressed by drought stress. The expression of the high-affinity NRT gene TaNRT2.1 was repressed by drought stress, but the expression of the other two high-affinity NRT genes, TaNRT2.2 and TaNRT2.3, was induced or repressed by soil drought depending on N application and growth stage. The expression of the genes TaAMT1.1 and TaAMT2.1 was mainly repressed by drought stress, whereas the expression of the gene TaAMT1.2 was induced by drought stress. The expression of TaNRT2.1 in XY107 was significantly higher than in XY6.

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