Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.

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

Sensors ◽

10.3390/s21165579 ◽

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.

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Optical sensing estimation of leaf nitrogen concentration in maize across a range of water-stress levels

Crop and Pasture Science ◽

10.1071/cp10374 ◽

2011 ◽

Vol 62 (6) ◽

pp. 474 ◽

Cited By ~ 9

Author(s):

Tong-Chao Wang ◽

B. L. Ma ◽

You-Cai Xiong ◽

M. Farrukh Saleem ◽

Feng-Min Li

Keyword(s):

Water Stress ◽

Vegetation Index ◽

Optical Sensing ◽

Leaf Nitrogen ◽

Growth Stages ◽

N Concentration ◽

Leaf N Concentration ◽

Chlorophyll Index ◽

Plant Moisture ◽

Leaf N

Optical sensing techniques offer an instant estimation of leaf nitrogen (N) concentration during the crop growing season. Differences in plant-moisture status, however, can obscure the detection of differences in N levels. This study presents a vegetation index that robustly measures differences in foliar N levels across a range of plant moisture levels. A controlled glasshouse study with maize (Zea mays L.) subjected to both water and N regimes was conducted in Ottawa, Canada. The purpose of the study was to identify spectral waveband(s), or indices derived from different wavebands, such as the normalised difference vegetation index (NDVI), that are capable of detecting variations in leaf N concentration in response to different water and N stresses. The experimental design includes three N rates and three water regimes in a factorial arrangement. Leaf chlorophyll content and spectral reflectance (400–1075 nm) were measured on the uppermost fully expanded leaves at the V6, V9 and V12 growth stages (6th, 9th and 12th leaves fully expanded). N concentrations of the same leaves were determined using destructive sampling. A quantitative relationship between leaf N concentration and the normalised chlorophyll index (normalised to well fertilised and well irrigated plants) was established. Leaf N concentration was also a linear function (R2 = 0.9, P < 0.01) of reflectance index (NDVI550, 760) at the V9 and V12 growth stages. Chlorophyll index increased with N nutrition, but decreased with water stress. Leaf reflectance at wavebands of 550 ± 5 nm and 760 ± 5 nm were able to separate water- and N-stressed plants from normal growing plants with sufficient water and N supply. Our results suggest that NDVI550, 760 and normalised chlorophyll index hold promise for the assessment of leaf N concentration at the leaf level of both normal and water-stressed maize plants.

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Diagnostic nitrogen concentrations for tomatoes grown in sand culture

Australian Journal of Experimental Agriculture ◽

10.1071/ea9880401 ◽

1988 ◽

Vol 28 (3) ◽

pp. 401 ◽

Cited By ~ 3

Author(s):

DO Huett ◽

G Rose

Keyword(s):

Critical Values ◽

Sand Culture ◽

Nutrient Concentrations ◽

Total N ◽

Nitrate N ◽

N Concentration ◽

Leaf N Concentration ◽

Petiole Sap ◽

N Status ◽

Leaf N

The tomato cv. Flora-Dade was grown in sand culture with 4 nitrogen (N) levels of 1.07-32.14 mmol L-1 applied as nitrate each day in a complete nutrient solution. The youngest fully opened leaf (YFOL) and remaining (bulked) leaves were harvested at regular intervals over the 16-week growth period. Standard laboratory leaf total and nitrate N determinations were conducted in addition to rapid nitrate determinations on YFOL petiole sap. The relationships between plant growth and leaf N concentration, which were significantly affected by N application level, were used to derive diagnostic leaf N concentrations. Critical and adequate concentrations in petiole sap of nitrate-N, leaf nitrate-N and total N for the YFOL and bulked leaf N were determined from the relationship between growth rate relative to maximum at each sampling time and leaf N concentration. YFOL petiole sap nitrate-N concentration, which can be measured rapidly in the field by using commercial test strips, gave the most sensitive guide to plant N status. Critical values of 770-1 120 mg L-I were determined over the 10-week period after transplanting (first mature fruit). YFOL (leaf + petiole) total N concentration was the most consistent indicator of plant N status where critical values of4.45-4.90% were recorded over the 4- 12 week period after transplanting (early harvests at 12 weeks). This test was less sensitive but more precise than the petiole sap nitrate test. The concentrations of N, potassium, phosphorus, calcium and magnesium in YFOL and bulked leaf corresponding to the N treatments producing maximum growth rates are presented, because nutrient supply was close to optimum and the leaf nutrient concentrations can be considered as adequate levels.

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How Nitrogen Supply Affects Growth and Nitrogen Uptake, Use Efficiency, and Loss from Citrus Seedlings

Journal of the American Society for Horticultural Science ◽

10.21273/jashs.121.1.105 ◽

1996 ◽

Vol 121 (1) ◽

pp. 105-114 ◽

Cited By ~ 14

Author(s):

John D. Lea-Cox ◽

James P. Syvertsen

Keyword(s):

Plant Growth ◽

N Uptake ◽

Single Application ◽

N Application ◽

N Supply ◽

N Concentration ◽

Leaf N Concentration ◽

Use Efficiency ◽

Complete Nutrient Solution ◽

Leaf N

We examined how N supply affected plant growth and N uptake, allocation and leaching losses from a fine sandy soil with four Citrus rootstock species. Seedlings of `Cleopatra' mandarin (Citrus reticulata Blanco) and `Swingle' citrumelo (C. paradisi × P. trifoliata) were grown in a glasshouse in 2.3-liter pots of Candler fine sand and fertilized weekly with a complete nutrient solution containing 200 mg N/liter (20 mg N/week). A single application of 15NH415NO3(17.8% atom excess 15N) was substituted for a normal weekly N application when the seedlings were 22 weeks old (day O). Six replicate plants of each species were harvested at 0.5, 1.5, 3.5, 7, 11, and 30 days after 15N application. In a second experiment, NH4 NO3 was supplied at 18,53, and 105 mg N/week to 14-week-old `Volkamer' lemon (C. volkameriana Ten. & Pasq.) and sour orange (C. aurantium L.) seedlings in a complete nutrient solution for 8 weeks. A single application of 15NH415NO3 (23.0% 15N) was substituted at 22 weeks (day 0), as in the first experiment, and seedlings harvested 3,7, and 31 days after 15N application. Nitrogen uptake and partitioning were similar among species within each rate, but were strongly influenced by total N supply and the N demand by new growth. There was no 15N retranslocation to new tissue at the highest (105 mg N/week) rate, but N supplies below this rate limited plant growth without short-term 15N reallocation from other tissues. Leaf N concentration increased linearly with N supply up to the highest rate, while leaf chlorophyll concentration did not increase above that at 53 mg N/week. Photosynthetic CO2 assimilation was not limited by N in this study; leaf N concentration exceeded 100 mmol·m-2 in all treatments. Thus, differences in net productivity at the higher N rates appeared to be a function of increased leaf area, but not of leaf N concentration. Hence, N use efficiency decreased significantly over the range of N supply, whether expressed either on a gas-exchange or dry weight basis. Mean plant 15N uptake efficiencies after 31 days decreased from 60% to 47% of the 15N applied at the 18,20, and 53 mg N/week rates to less than 33% at the 105 mg N/week rate. Leaching losses increased with N rate, with plant growth rates and the subsequent N requirements of these Citrus species interacting with residual soil N and potential leaching loss.

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Effect of spad techniques and planting density on 'Y' leaf nitrogen concentration in transplanted rice

Acta Agronomica Hungarica ◽

10.1556/aagr.52.2004.1.11 ◽

2004 ◽

Vol 52 (1) ◽

pp. 95-104 ◽

Cited By ~ 1

Author(s):

P. Janaki ◽

T. M. Thiyagarajan

Keyword(s):

Grain Yield ◽

Field Experiments ◽

Nitrogen Concentration ◽

Planting Density ◽

Growth Stages ◽

Chlorophyll Meter ◽

N Concentration ◽

Leaf N Concentration ◽

N Management ◽

Leaf N

Field experiments were conducted in June-September 1998 and 1999 with rice variety ASD18 at the wetland farm of Tamil Nadu Agricultural University, in Coimbatore, India to examine variations in 'Y' leaf (youngest fully expanded leaf) N concentration as influenced by different planting densities and N management strategies in a split plot design. The main plot consisted of three plant populations (33, 66 and 100 hills m-2) and the sub-plots treatments of five N management approaches. The results revealed that the nitrogen concentration progressively declined with growth, the decline being steep up to 35 days after transplanting, wereafter the values became almost linear up to the flowering stage in all the treatments. The mean 'Y' leaf N was found to be significantly higher at 33 hills m-2 (45.1 g kg-1), while the other two densities were on par (42.9 g kg-1). When N application was based on chlorophyll meter (SPAD) values the leaf N concentration was maintained at a level of 39.2 to 51.9 g kg-1 to produce maximum grain yield. A significant correlation was observed between the chlorophyll meter values and 'Y' leaf N concentrations at various days after transplanting (r values ranged from 0.57* to 0.83**), while the correlation was highly significant during the major physiological growth stages. Though the 'Y' leaf content was significantly higher in the treatment involving Sesbania rostrata green manuring + 150 kg N applied in splits, the grain yield produced was on par in all the N applied treatments. A highly significant correlation was observed between the grain yield and both 'Y' leaf N content and SPAD values during various growth periods.

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Feasibility Study of Using Absolute SPAD Values for Standardized Evaluation of Corn Nitrogen Status

Nitrogen ◽

10.3390/nitrogen2030020 ◽

2021 ◽

Vol 2 (3) ◽

pp. 298-307

Author(s):

Abdelaziz Rhezali ◽

Abdellah El Aissaoui

Keyword(s):

Zea Mays L ◽

Growth Stages ◽

Plant Analysis ◽

N Concentration ◽

Fertilizer Recommendations ◽

Leaf N Concentration ◽

The Mean ◽

High Yields ◽

Spad Meter ◽

Leaf N

Nitrogen fertilizer recommendations for corn (Zea mays L.) should ensure high yields using adequate N doses. Soil–plant analysis development (SPAD) meter technology using absolute SPAD values, might be more reliable than relative SPAD values in helping corn producers making timely decisions about N applications. This study aimed to develop a relationship between absolute SPAD values and leaf N concentration, and to determine optimal leaf N concentrations at early corn growth stages (V6, V8, V10, and V12). Three experiments were conducted during two summer seasons (2014 and 2015) using six N treatments applied at early corn growth stages. In parallel, two experiments were carried out in a high residual N environment to establish the optimum corn leaf N concentration. Results showed a significant linear relationship between corn leaf N concentrations and absolute SPAD values (R2 = 0.80, p < 0.05). The mean optimum corn leaf N concentration decreased over corn growth stages. It is of great importance to make the absolute SPAD method accessible for farmers, but more research is required to perform standardized reading of absolute SPAD values data.

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Nitrogen Management Based on Visible/Near Infrared Spectroscopy in Pear Orchards

Remote Sensing ◽

10.3390/rs13050927 ◽

2021 ◽

Vol 13 (5) ◽

pp. 927

Author(s):

Jie Wang ◽

Xiaojun Shi ◽

Yangchun Xu ◽

Caixia Dong

Keyword(s):

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Fertilizer Application ◽

First Year ◽

N Application ◽

N Concentration ◽

Application Rates ◽

Leaf N Concentration ◽

Leaf N

The timely estimation of nitrogen (N) requirements is essential for managing N fertilizer application in pear orchards. Visible/near infrared spectroscopy is a non-destructive and effective technique for real-time assessment of leaf N concentration, but its utility for decisions about fertilizer application in the pear orchards remains to be determined. In this study, we used leaf spectroscopy to determine leaf N concentration, used this value to calculate the amounts of N required for supplementary fertilization, and then evaluated the effects of the application. Over the two-year study, Cuiguan pear trees were treated with N at the following rates: 0 (N0), 100 (N1), 200 (N2), 300 (N3), and 400 (N4) g N per tree, regarded as five “controlled” N application rates. Another four “regulatory” treatments (Nr1-4) were fertilized as the “controlled” N application rates the first year, then given adjusted N application by topdressing as calculated using the N concentrations inferred from visible/near infrared spectroscopy data the second year. A model (R2 = 0.82) was established the first year to relate leaf spectra and N concentration using a partial least squares regression with full bands (350–2500 nm). The amount of N in the topdressing for the supplemental treatments was determined using the predicted leaf N concentration and the topdressing calculation method adapted from the N balance formula. Results showed that adjusted N applications of the Nr1 and Nr2 increased yield by 26% and 23%, respectively, over the controlled treatments N1 and N2. Although treatments Nr3 and Nr4 did not increase yield significantly over N3 and N4, the partial factor productivity of nitrogen in Nr4 was higher than the N4. The transverse diameter of fruit from Nr1 trees was significantly higher than from N1 trees, while the longitudinal diameter of fruit from Nr1, Nr2, and Nr3 trees was significantly higher than from N1, N2 and N3 trees, suggesting that fruit longitudinal growth and single-fruit weight is stimulated by adjusted N applications. However, the soluble solids in fruit from trees receiving adjusted N were not significantly greater than in fruit from non-supplemented trees. In conclusion, our results illustrate that regulatory N management contributes to fruit yield and quality especially in the nitrogen deficiency condition and improves the nitrogen use efficiency in nitrogen surplus. The N prediction model established using the nondestructive visible/near infrared spectroscopy is convenient and economical.

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Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield

Remote Sensing ◽

10.3390/rs14010120 ◽

2021 ◽

Vol 14 (1) ◽

pp. 120

Author(s):

Razieh Barzin ◽

Hossein Lotfi ◽

Jac J. Varco ◽

Ganesh C. Bora

Keyword(s):

Machine Learning ◽

Grain Yield ◽

Vegetation Index ◽

Growth Stages ◽

Fertilizer N ◽

Red Edge ◽

Leaf N Concentration ◽

Different Growth Stages ◽

N Status ◽

Leaf N

Applying the optimum rate of fertilizer nitrogen (N) is a critical factor for field management. Multispectral information collected by active canopy sensors can potentially indicate the leaf N status and aid in predicting grain yield. Crop Circle multispectral data were acquired with the purpose of measuring the reflectance data to calculate vegetation indices (VIs) at different growth stages. Applying the optimum rate of fertilizer N can have a considerable impact on grain yield and profitability. The objectives of this study were to evaluate the reliability of a handheld Crop Circle ACS-430, to estimate corn leaf N concentration and predict grain yield of corn using machine learning (ML) models. The analysis was conducted using four ML models to identify the best prediction model for measurements acquired with a Crop Circle ACS-430 field sensor at three growth stages. Four fertilizer N levels from deficient to excessive in 50/50 spilt were applied to corn at 1–2 leaves, with visible leaf collars (V1-V2 stage) and at the V6-V7 stage to establish widely varying N nutritional status. Crop Circle spectral observations were used to derive 25 VIs for different growth stages (V4, V6, and VT) of corn at the W. B. Andrews Agricultural Systems farm of Mississippi State University. Multispectral raw data, along with Vis, were used to quantify leaf N status and predict the yield of corn. In addition, the accuracy of wavelength-based and VI-based models were compared to examine the best model inputs. Due to limited observed data, the stratification approach was used to split data to train and test set to obtain balanced data for each stage. Repeated cross validation (RCV) was then used to train the models. Results showed that the Simplified Canopy Chlorophyll Content Index (SCCCI) and Red-edge ratio vegetation index (RERVI) were the most effective VIs for estimating leaf N% and that SCCCI, Red-edge chlorophyll index (CIRE), RERVI, Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Vegetation Index (NDVI) were the most effective VIs for predicting corn grain yield. Additionally, among the four ML models utilized in this research, support vector regression (SVR) achieved the most accurate results for estimating leaf N concentration using either spectral bands or VIs as the model inputs.

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Quantifying the nonlinearity in chlorophyll meter response to corn leaf nitrogen concentration

Canadian Journal of Plant Science ◽

10.4141/cjps95-030 ◽

1995 ◽

Vol 75 (1) ◽

pp. 179-182 ◽

Cited By ~ 81

Author(s):

L. M. Dwyer ◽

D. W. Stewart ◽

E. Gregorich ◽

A. M. Anderson ◽

B. L. Ma ◽

...

Keyword(s):

Growth Stage ◽

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Growth Stages ◽

Concentration Data ◽

Chlorophyll Meter ◽

N Concentration ◽

Leaf N Concentration ◽

Non Destructive ◽

Leaf N

Chlorophyll meters have been used to provide a rapid non-destructive method to estimate corn leaf nitrogen (N) concentration, although meter readings plateau at high leaf N levels. Paired chlorophyll meter and leaf N concentration data were obtained for ear level leaves at growth stages ranging from 3 wk before anthesis to 5 wk after anthesis over a 2-yr period at Ottawa, Ontario. Separate quadratic-plus-plateau models best represented chlorophyll meter response to leaf N concentration for pre-anthesis, early grain-fill and late grain-fill stages; chlorophyll meter readings corresponding to the beginning of the plateau increased at later growth stages. Leaf N concentration was estimated well from chlorophyll meter readings up to the plateau range using growth stage specific functions (R2 ≥ 0.77) but chlorophyll meter readings beyond the plateau should not be used to estimate leaf N concentration. Key words: Chlorophyll meter calibration, maize

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