Remote Estimation of Nitrogen Vertical Distribution by Consideration of Maize Geometry Characteristics

The vertical leaf nitrogen (N) distribution in the crop canopy is considered to be an important adaptive response of crop growth and production. Remote sensing has been widely applied for the determination of a crop’s N status. Some studies have also focused on estimating the vertical leaf N distribution in the crop canopy, but these analyses have rarely considered the plant geometry and its influences on the remote estimation of the N vertical distribution in the crop canopy. In this study, field experiments with three types of maize (Zea mays L.) plant geometry (i.e., horizontal type, intermediate type, and upright type) were conducted to demonstrate how the maize plant geometry influences the remote estimation of N distribution in the vertical canopy (i.e., upper layer, middle layer, and bottom layer) at different growth stages. The results revealed that there were significant differences among the three maize plant geometry types in terms of canopy architecture, vertical distribution of leaf N density (LND, g m−2), and the LND estimates in the leaves of different layers based on canopy hyperspectral reflectance measurements. The upright leaf variety had the highest correlation between the lower-layer LND (R2 = 0.52) and the best simple ratio (SR) index (736, 812), and this index performed well for estimating the upper (R2 = 0.50) and middle (R2 = 0.60) layer LND. However, for the intermediate leaf variety, only 25% of the variation in the lower-layer LND was explained by the best SR index (721, 935). The horizontal leaf variety showed little spectral sensitivity to the lower-layer LND. In addition, the growth stages also affected the remote detection of the lower leaf N status of the canopy, because the canopy reflectance was dominated by the biomass before the 12th leaf stage and by the plant N after this stage. Therefore, we can conclude that a more accurate estimation of the N vertical distribution in the canopy is obtained by canopy hyperspectral reflectance when the maize plants have more upright leaves.

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

Remote Sensing ◽

10.3390/rs12071139 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1139

Author(s):

Rui Dong ◽

Yuxin Miao ◽

Xinbing Wang ◽

Zhichao Chen ◽

Fei Yuan ◽

...

Keyword(s):

Precision Agriculture ◽

Fluorescence Sensor ◽

Accurate Estimation ◽

Growth Stages ◽

Fluorescence Sensors ◽

N Application ◽

N Concentration ◽

Leaf N Concentration ◽

N Status ◽

Leaf N

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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Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms

Scientific Reports ◽

10.1038/s41598-020-73745-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Hiroto Yamashita ◽

Rei Sonobe ◽

Yuhei Hirono ◽

Akio Morita ◽

Takashi Ikka

Keyword(s):

Machine Learning ◽

Learning Algorithms ◽

Visible Region ◽

Machine Learning Algorithms ◽

Hyperspectral Reflectance ◽

N Content ◽

Green Leaves ◽

Leaf N Content ◽

N Status ◽

Leaf N

Abstract Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325–1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.

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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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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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Monitoring leaf nitrogen in wheat using canopy reflectance spectra

Canadian Journal of Plant Science ◽

10.4141/p05-157 ◽

2006 ◽

Vol 86 (4) ◽

pp. 1037-1046 ◽

Cited By ~ 41

Author(s):

Yan Zhu ◽

Yingxue Li ◽

Wei Feng ◽

Yongchao Tian ◽

Xia Yao ◽

...

Keyword(s):

Nitrogen Concentration ◽

Leaf Nitrogen ◽

Reflectance Spectra ◽

Nutrition Status ◽

Nitrogen Accumulation ◽

Canopy Reflectance ◽

Leaf Nitrogen Concentration ◽

Non Destructive ◽

N Status ◽

Leaf N

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

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Distribusi Vertikal Logam Pb, Zn, Cr dan Keterkaitannya Terhadap Karbon Organik Sedimen di Pantai Marunda, Jakarta

BULETIN OSEANOGRAFI MARINA ◽

10.14710/buloma.v9i2.27283 ◽

2020 ◽

Vol 9 (2) ◽

pp. 125-132

Author(s):

Triyanti Nurhidayah ◽

Lilik Maslukah ◽

Sri Yulina Wulandari ◽

Kurnia Kurnia

Keyword(s):

Heavy Metals ◽

Vertical Distribution ◽

Lower Layer ◽

Middle Layer ◽

Average Concentration ◽

Anthropogenic Activity ◽

Sediment Texture ◽

The Relationship ◽

The Vertical Distribution ◽

Very High

Pantai Marunda terletak di Teluk Jakarta dan berdekatan dengan muara Sungai Tiram. Kegiatan antropogenik di sekitar Pantai Marunda sangat tinggi, sehingga dapat menyumbang limbah yang mengandung logam berat Pb, Zn dan Cr. Limbah logam berat yang terakumulasi dalam perairan, akan mengendap dalam sedimen dan seiring berjalannya waktu akan mengalami peningkatan. Tujuan dari penelitian ini, untuk mengetahui konsentrasi Pb, Zn, dan Cr berdasarkan kedalaman vertikal, hubungannya dengan karbon organik total (KOT) dan ukuran butir sedimen di Pantai Marunda. Sampel sedimen diambil menggunakan polietylen sediment core dan dipisahkan berdasarkan kedalamannya (1-3 cm, 4-6 cm, dan 7-9 cm). Logam berat dalam sedimen dianalisis menggunakan metode destruksi asam dan diukur nilai absorbansinya menggunakan AAS, karbon organik total menggunakan metode loss of ignition (LOI) dan analisa tekstur sedimen dengan metode pengayakan dilanjutkan pemipetan. Hasil menunjukkan bahwa konsentrasi rerata logam berat Pb, Zn, Cr secara berurutan pada lapisan atas sebesar 5,14; 107,19; dan 12,79 ppm, lapisan tengah sebesar 4,41; 100,20; 12,28 ppm serta lapisan bawah sebesar 4,8; 101,30; 14,10 ppm. Logam berat Zn dan Cr berkorelasi positif kuat terhadap KOT dan persentase lumpur, sedangkan terhadap Pb berkorelasi negatif. Hasil penelitian ini menggambarkan bahwa distribusi logam berat Pb, Zn dan Cr secara vertikal menunjukkan konsentrasi tinggi pada lapisan permukaan yaitu pada kedalaman sedimen 1-3 cm dan keberadaanya ditentukan oleh konsesntrasi KOT dan fraksi sedimen jenis lumpur. The Marunda Beach is located on the Jakarta Bay and adjacent to the mouth of the Tiram River. Anthropogenic activity around Marunda Beach is very high, so it can contribute the heavy metals such as Pb, Zn and Cr. The heavy metal will accumulate in the sediment and over time will be increase. The purpose of this study was to determine the vertical distribution of Pb, Zn, Cr, and their relationship to total organic carbon (TOC) and the grain size. Sediment samples were taken using polyethylene cores and this sample separated based on their depth (1-3 cm, 4-6 cm, and 7-9 cm). The heavy metals were analyzed using acid destruction and absorbance values were measured using AAS, TOC using the loss of ignition (LOI) and sediment texture with a sifting method, followed by pipetting. The results showed that the average concentration of Pb, Zn, Cr in the upper layer was 5.14; 107.19; 12.79 ppm, in the middle layer of 4.41; 100.20; 12.28 ppm and in the lower layer 4.8; 101.30; 14.10 ppm, respectively. Zn and Cr are strongly positively correlated to TOC and mud, and vice versa, the relationship to Pb is negative. The results of this study found that the vertical distribution of Pb, Zn and Cr was high in the surface layer (1-3 cm) and their presence was determined by TOC concentration and mud fraction.

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Estimating Vertical Distribution of Leaf Water Content within Wheat Canopies after Head Emergence

Remote Sensing ◽

10.3390/rs13204125 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4125

Author(s):

Weiping Kong ◽

Wenjiang Huang ◽

Lingling Ma ◽

Lingli Tang ◽

Chuanrong Li ◽

...

Keyword(s):

Vertical Distribution ◽

Water Content ◽

Near Infrared ◽

Field Experiments ◽

Estimation Method ◽

Middle Layer ◽

Leaf Water ◽

Leaf Water Content ◽

Growth Stages ◽

Spectral Indices

Monitoring vertical profile of leaf water content (LWC) within wheat canopies after head emergence is vital significant for increasing crop yield. However, the estimation of vertical distribution of LWC from remote sensing data is still challenging due to the effects of wheat spikes and the efficacy of sensor measurement from the nadir direction. Using two-year field experiments with different growth stages after head emergence, N rates, wheat cultivars, we investigated the vertical distribution of LWC within canopies, the changes of canopy reflectance after spikes removal, the relationship between spectral indices and LWC in the upper-, middle- and bottom-layer. The interrelationship among vertical LWC were constructed, and four ratio of reflectance difference (RRD) type of indices were proposed based on the published WI and NDWSI indices to determine vertical distribution of LWC. The results indicated a bell shape distribution of LWC in wheat plants with the highest value appeared at the middle layer, and significant linear correlations between middle-LWC vs. upper-LWC and middle-LWC vs. bottom-LWC (r ≥ 0.92) were identified. The effects of wheat spikes on spectral reflectance mainly occurred in near infrared to shortwave infrared regions, which then decreased the accuracy of LWC estimation. Spectral indices at the middle layer outperformed the other two layers in LWC assessment and were less susceptible to wheat spikes effects, in particular, the newly proposed narrow-band WI-4 and NDWSI-4 indices exhibited great potential in tracking the changes of middle-LWC (R2 = 0.82 and 0.84, respectively). By taking into account the effects of wheat spikes and the interrelationship of vertical LWC within canopies, an indirect induction strategy was developed for modeling the upper-LWC and bottom-LWC. It was found that the indirect induction models based on the WI-4 and NDWSI-4 indices were more effective than the models obtained from conventional direct estimation method, with R2 of 0.78 and 0.81 for the upper-LWC estimation, and 0.75 and 0.74 for the bottom-LWC estimation, respectively.

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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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Effect of tree leaf N status and N application time on yield and fruit N partitioning of mango

Acta Horticulturae ◽

10.17660/actahortic.2017.1183.22 ◽

2017 ◽

pp. 161-166

Author(s):

D. Hamilton ◽

C. Martin ◽

M. Bennet ◽

M. Hearnden ◽

C.A. Asis

Keyword(s):

Application Time ◽

N Application ◽

N Partitioning ◽

Tree Leaf ◽

N Status ◽

Leaf N

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LEAF TOTAL NITROGEN CONCENTRATION AS AN INDICATOR OF NITROGEN STATUS FOR PLANTLETS AND YOUNG PLANTS OF EUCALYPTUS CLONES

Revista Brasileira de Ciência do Solo ◽

10.1590/01000683rbcs20140689 ◽

2015 ◽

Vol 39 (4) ◽

pp. 1127-1140 ◽

Cited By ~ 4

Author(s):

Eric Victor de Oliveira Ferreira ◽

Roberto Ferreira Novais ◽

Bruna Maximiano Médice ◽

Nairam Félix de Barros ◽

Ivo Ribeiro Silva

Keyword(s):

Total Nitrogen ◽

Block Design ◽

Nitrogen Concentration ◽

Soil N ◽

Total N ◽

Chlorophyll Meter ◽

N Concentration ◽

Total Nitrogen Concentration ◽

N Status ◽

Leaf N

The use of leaf total nitrogen concentration as an indicator for nutritional diagnosis has some limitations. The objective of this study was to determine the reliability of total N concentration as an indicator of N status for eucalyptus clones, and to compare it with alternative indicators. A greenhouse experiment was carried out in a randomized complete block design in a 2 × 6 factorial arrangement with plantlets of two eucalyptus clones (140 days old) and six levels of N in the nutrient solution. In addition, a field experiment was carried out in a completely randomized design in a 2 × 2 × 2 × 3 factorial arrangement, consisting of two seasons, two regions, two young clones (approximately two years old), and three positions of crown leaf sampling. The field areas (regions) had contrasting soil physical and chemical properties, and their soil contents for total N, NH+4-N, and NO−3-N were determined in five soil layers, up to a depth of 1.0 m. We evaluated the following indicators of plant N status in roots and leaves: contents of total N, NH+4-N, NO−3-N, and chlorophyll; N/P ratio; and chlorophyll meter readings on the leaves. Ammonium (root) and NO−3-N (root and leaf) efficiently predicted N requirements for eucalyptus plantlets in the greenhouse. Similarly, leaf N/P, chlorophyll values, and chlorophyll meter readings provided good results in the greenhouse. However, leaf N/P did not reflect the soil N status, and the use of the chlorophyll meter could not be generalized for different genotypes. Leaf total N concentration is not an ideal indicator, but it and the chlorophyll levels best represent the soil N status for young eucalyptus clones under field conditions.

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