scholarly journals Application of a Combined Transmittance/Fluorescence Leaf Clip Sensor for the Nondestructive Determination of Nitrogen Status in White Cabbage Plants

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 482
Author(s):  
Stanisław Kaniszewski ◽  
Artur Kowalski ◽  
Jacek Dysko ◽  
Giovanni Agati
Keyword(s):  
Precision Agriculture ◽  
Leaf Nitrogen ◽  
Economic Advantage ◽  
Vegetable Crops ◽  
Non Invasive ◽  
White Cabbage ◽  
N Rates ◽  
N Status ◽  
Leaf N

The correct fertilization of vegetable crops is commonly determined on the basis of soil and plant costly destructive analyses, demanding more sustainable non-invasive optical detection. Here, we tested the ability of the combined transmittance/fluorescence leaf clip Dualex device for determining the nitrogen (N) status of cabbage plants. Fully developed leaves from plants grown under different N rates of 0; 100; 200; 300 kg N ha−1 in 2018 and 2019 were measured in the field by the Dualex sensor twice a year in July and October. The chlorophyll (Chl) and nitrogen (nitrogen balance index, NBI) indices and the flavonols (Flav) index of the sensor were positively and negatively correlated to leaf nitrogen, respectively. Merging the two-years data, the NBI versus leaf N correlation was less point dispersed in October than July (R2 = 0.76 and 0.64, respectively). NBI was also correlated to cabbage yield, better in July than October. Our results showed that the multiparametric Dualex device can be used as precision agriculture tool for the early prediction of plant N and cabbage yield with economic advantage for the growers and reduced environmental contamination due to nitrate leaching.

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

2006 ◽  
Vol 86 (4) ◽  
pp. 1037-1046 ◽  
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

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

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.

scholarly journals A Rapid Nondestructive Technique to Predict Leaf Nitrogen Status of Grapefruit Tree with Various Nitrogen Fertilization Practices

1998 ◽  
Vol 8 (1) ◽  
pp. 81-86 ◽  
Author(s):  
Y.C. Li ◽  
A.K. Alva ◽  
D.V. Calvert ◽  
M. Zhang
Keyword(s):  
Yield Response ◽  
Nitrogen Status ◽  
Nondestructive Technique ◽  
Fertilization Program ◽  
Irrigation Treatments ◽  
N Rates ◽  
Spad Readings ◽  
N Status ◽  
Spad Meter ◽  
Leaf N

The objective of this study was to evaluate the use of a leaf chlorophyll meter (SPAD-502) to predict nitrogen status and yield response of grapefruit trees. The study included two irrigation treatments, three sources, and four rates of fertilizer [dry soluble granular fertilizer broadcast or through fertigation with N rates either 50, 100, 150 lb/acre/yr (56, 112, or 168 kg.ha-1.yr-1)], and controlled-release fertilizer with N rates either 25, 50, 100 lb/acre. Irrigation treatments did not affect SPAD readings and leaf N concentrations. Correlations between SPAD readings and leaf N were greater for the spring flush (r2 = 0.61) than for the summer flush (r2 = 0.49). High correlations of SPAD meter readings with either fruit yields or leaf N suggests that SPAD meter reading is an acceptable index of N status and fruit production of the trees. SPAD readings can be made rapidly without destructive sampling of the leaves. This study demonstrates that the SPAD meter can be used to evaluate the tree N status and improve a N fertilization program for grapefruit trees.

Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using Artificial Neural Networks

2014 ◽  
Vol 5 (2) ◽  
pp. 38-49
Author(s):  
Claudio Kapp Junior ◽  
Eduardo Fávero Caires ◽  
Alaine Margarete Guimarães
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Biomass Production ◽  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Wheat Crop ◽  
Nitrogen Status ◽  
Artificial Neural ◽  
N Rates

Precision Agriculture has the goal of reducing cost which is difficult when it is related to fertilizers application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizers application present significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the Resilient Propagation and Backpropagation Artificial Neural Networks algorithms. As a result was detected a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants. For both algorithms it was established a satisfactory model for estimating wheat dry biomass production. The best Backpropagation and Resilient Propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.

Discriminating Biomass and Nitrogen Status in Wheat Crop by Spectral Reflectance Using ANN Algorithms

2018 ◽  
pp. 156-172
Author(s):  
Claudio Kapp Jr. ◽  
Eduardo Fávero Caires ◽  
Alaine Margarete Guimarães
Keyword(s):  
Biomass Production ◽  
Precision Agriculture ◽  
Spectral Reflectance ◽  
Fertilizer Application ◽  
Wheat Crop ◽  
Nitrogen Status ◽  
Propagation Models ◽  
Dry Biomass ◽  
N Rates

Precision agriculture has the goal of reducing cost which is difficult when it is related to fertilizer application. Nitrogen (N) is the nutrient absorbed in greater amounts by crops and the N fertilizer application presents significant costs. The use of spectral reflectance sensors has been studied to identify the nutritional status of crops and prescribe varying N rates. This study aimed to contribute to the determination of a model to discriminating biomass and nitrogen status in wheat through two sensors, GreenSeeker and Crop Circle, using the resilient propagation and backpropagation artificial neural networks algorithms. As a result, a strong correlation to the sensor readings with the aboveground biomass production and N extraction by plants was detected. For both algorithms a satisfactory model for estimating wheat dry biomass production was established. The best backpropagation and resilient propagation models defined showed better performance for the GreenSeeker and Crop Circle sensors, respectively.

scholarly journals Using SPAD-502 Values to Assess the Nitrogen Status of Apple Trees

HortScience ◽  
1995 ◽  
Vol 30 (3) ◽  
pp. 508-512 ◽  
Author(s):  
Denise Neilsen ◽  
Eugene J. Hogue ◽  
Gerald H. Neilsen ◽  
Peter Parchomchuk
Keyword(s):  
Growing Season ◽  
Sampling Time ◽  
Nutrition Programs ◽  
N Concentration ◽  
Leaf N Concentration ◽  
N Rates ◽  
Spad Readings ◽  
Spad Meter ◽  
Leaf N

Four apple (Malus domestica Borkh) cultivars (`Fuji', `Spartan', `Fiesta', and `Gala') on Malling 9 (M.9) rootstock were grown in the field with three N rates (5, 20, and 35 g N/tree per year), supplied as Ca(NO3)2, and fertigated daily for 9 weeks. In the second year, leaf SPAD readings (chlorophyll readings obtained with the Minolta-502 SPAD meter) increased over the growing season for all cultivars, and leaf N decreased. Leaf SPAD and leaf N measurements increased in response to N fertigation rate at all sampling times. `Gala' consistently had lower SPAD readings than the other cultivars, and, with the exception of the first sampling time, `Fuji' had higher and `Fiesta' lower leaf N concentrations than other cultivars. There were strong relationships between leaf N concentration and SPAD readings for all cultivars until mid-July (r2 = 0.44 to 0.89), but not later in the growing season. Differences in SPAD readings and leaf N concentration due to cultivar and over time were as great as those due to N treatments, indicating that in the future, determination of critical SPAD values for apple leaves must be standardized for cultivar and sampling time. SPAD readings could be used to assess the need for N early in the growing season in fertigated orchards where rapid changes in nutrition programs can be undertaken readily.

scholarly journals Response of ‘Italian Riesling’ Leaf Nitrogen Status and Fruit Composition (Vitis vinifera L.) to Foliar Nitrogen Fertilization

HortScience ◽  
2016 ◽  
Vol 51 (3) ◽  
pp. 262-267 ◽  
Author(s):  
Danijela Janjanin ◽  
Marko Karoglan ◽  
Mirjana Herak Ćustić ◽  
Marijan Bubola ◽  
Mirela Osrečak ◽  
...  
Keyword(s):  
Amino Nitrogen ◽  
Grape Juice ◽  
Leaf Nitrogen ◽  
Vitis Vinifera L ◽  
Fruit Composition ◽  
Yeast Assimilable Nitrogen ◽  
Leaf N Content ◽  
N Compounds ◽  
N Status ◽  
Leaf N

Two-year study was conducted on Italian Riesling cultivar with the aim to compare the effect of foliar sprays with different nitrogen forms on grapevine leaf N status, yield, and nitrogen compounds in grape juice. Treatments included no fertilization (control), soil NPK treatment, and three foliar treatments [amino acids, urea, ammonia (NH4+)/nitrate] applied four times during season, also treated with soil NPK. The application of 1% w/v urea significantly increased leaf total leaf N content in the second year of study. However, there were no effects on N compounds in grape juice, since changes in free amino nitrogen (FAN), NH4+, and consequently yeast assimilable nitrogen (YAN) were not consistent among the treatments and experimental years. Although increase of vine leaf N status was achieved by 1% w/v urea, additional modifying of application time (by moving it closer to veraison) is needed, with the aim to increase N compounds in grape juice as well.

scholarly journals Use of an Active Canopy Sensor and SPAD Chlorophyll Meter to Quantify Geranium Nitrogen Status

HortScience ◽  
2012 ◽  
Vol 47 (1) ◽  
pp. 45-50 ◽  
Author(s):  
Yun-wen Wang ◽  
Bruce L. Dunn ◽  
Daryl B. Arnall ◽  
Pei-sheng Mao
Keyword(s):  
Green Leaf ◽  
Fertilizer Treatment ◽  
N Content ◽  
Chlorophyll Meter ◽  
N Concentration ◽  
Flower Traits ◽  
Leaf N Content ◽  
N Rates ◽  
N Status ◽  
Leaf N

This research was conducted to investigate the potentials of normalized difference vegetation index (NDVI), a Soil-Plant Analyses Development (SPAD) chlorophyll meter, and leaf nitrogen (N) concentration [% dry matter (DM)] for rapid determination of N status in potted geraniums (Pelargonium ×hortorum). Two F1 cultivars were chosen to represent a dark-green leaf cultivar, Horizon Deep Red, and a light-green leaf cultivar, Horizon Tangerine, and were grown in a soilless culture system. All standard 6-inch (15.24-cm) pots filled with a medium received an initial top-dress application of 5 g controlled-release fertilizer (15N–9P–12K), then plants were supplemented with additional N in the form of urea at 0, 50, 100, or 200 mg·L−1 N every few days to produce plants ranging from N-deficient to N-sufficient. The NDVI readings of individual plants from a NDVI pocket sensor developed by Oklahoma State University were collected weekly until the flowering stage. Data on flower traits, including number of pedicels (NOP), number of full umbels per pot (NOFU), total number of flowers per pot (TNF), number of flowers per pedicel (NOF), and inflorescences diameter (IFD), were collected 3 months after initial fertilizer treatment. After measuring flower traits, pedicels were removed from each pot, and SPAD value, NDVI, and leaf N concentration (g·kg−1 DM) were measured simultaneously. Cultivar and N rate significantly affected all but two flower and one N status parameters studied. The coefficient of determination R2 showed that NOP, NOFU, and TNF traits were more related to the N rates and the status parameters studied for ‘Horizon Tangerine’ than for ‘Horizon Deep Red’. For the latter cultivar, NOP and TNF traits were highly related to NDVI and SPAD values than N rates and leaf N content parameters. Correlation analysis indicated that the NDVI readings (R2 = 0.848 and 0.917) and SPAD values (R2 = 0.861 and 0.950) were significantly related to leaf N content (g·kg−1 DM) between cultivars. However, sensitivity of the NDVI and chlorophyll values to N application rate in geranium was slightly less than leaf N content. Strong correlations (R2 = 0.974 and 0.979, respectively) between NDVI and SPAD values were found within cultivars. The results demonstrated NDVI and SPAD values can be used to estimate N status in geranium. Because the pocket NDVI sensor will be cheaper than the SPAD unit, it has an advantage in determining N content in potted ornamentals.

Quantitative relationships of leaf nitrogen status to canopy spectral reflectance in rice

2007 ◽  
Vol 58 (11) ◽  
pp. 1077 ◽  
Author(s):  
Yan Zhu ◽  
Dongqin Zhou ◽  
Xia Yao ◽  
Yongchao Tian ◽  
Weixing Cao
Keyword(s):  
Spectral Reflectance ◽  
Vegetation Indices ◽  
Leaf Nitrogen ◽  
Canopy Reflectance ◽  
Rice Cultivars ◽  
Canopy Spectral Reflectance ◽  
Mean Square Errors ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive and quick methods for assessing leaf nitrogen (N) status are helpful for precision N management in field crops. The present study was conducted to determine the quantitative relationships of leaf N concentration on a leaf dry weight basis (LNC) and leaf N accumulation per unit soil area (LNA) to ground-based canopy spectral reflectance in rice (Oryza sativa L.). Time-course measurements were taken on canopy spectral reflectance, LNC, and leaf dry weights, with 4 field experiments under different N application rates and rice cultivars across 4 growing seasons. All possible ratio vegetation indices (RVI), difference vegetation indices (DVI), and normalised difference vegetation indices (NDVI) of key wavebands from the MSR16 radiometer were calculated. The results showed that LNC, LNA, and canopy reflectance spectra all markedly varied with N rates, with consistent change patterns among different rice cultivars and experiment years. There were highly significant linear correlations between LNC and canopy reflectance in the visible region from 560 to 710 nm (|r| > 0.85), between LNA and canopy reflectance from 760 to 1100 nm (|r| > 0.79), and from 460 to 710 nm wavelengths (|r| > 0.70). Among all possible RVI, DVI, and NDVI of key wavebands from the MSR16 radiometer, NDVI of 1220 and 710 nm was most highly correlated to LNC, and RVI of 950 and 660 nm and RVI of 950 and 680 nm were the best spectral indices for quantitative monitoring of LNA in rice. The average relative root mean square errors (RRMSE) between the predicted LNC and LNA and the observed values with independent data were no more than 11% and 25%, respectively. These results indicated that the canopy spectral reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in rice.

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