Potential Use of Nitrogen Reflectance Index to estimate Plant Parameters and Yield of Maize

Diker, Kenan; Bausch, Walter C.

doi:10.1016/s1537-5110(03)00097-7

Cited by 47 publications

(38 citation statements)

References 12 publications

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“…Remote sensing technique has been recognized as a reliable method to estimate various variables related to physiology and biochemistry (Hinzman et al, 1986;McMurtrey et al, 1994;Casanova et al, 1998;Diker and Bausch, 2003;Chen et al, 2007). Measurement of spectral reflectance is non-destructive, rapid and relatively cheap, and can be applied across spatial scales (Gamon and Qiu, 1999), so remote sensing has attracted a great deal of attention in terms of application for crop monitoring so far (Casanova et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Qian

Huang

Wang

et al. 2008

J. Zhejiang Univ. Sci. B

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Abstract:To further develop the methods to remotely sense the biochemical content of plant canopies, we report the results of an experiment to estimate the concentrations of three biochemical variables of corn, i.e., nitrogen (N), crude fat (EE) and crude fiber (CF) concentrations, by spectral reflectance and the first derivative reflectance at fresh leaf scale. The correlations between spectral reflectance and the first derivative transformation and three biochemical variables were analyzed, and a set of estimation models were established using curve-fitting analyses. Coefficient of determination (R²), root mean square error (RMSE) and relative error of prediction (REP) of estimation models were calculated for the model quality evaluations, and the possible optimum estimation models of three biochemical variables were proposed, with R 2 being 0.891, 0.698 and 0.480 for the estimation models of N, EE and CF concentrations, respectively. The results also indicate that using the first derivative reflectance was better than using raw spectral reflectance for all three biochemical variables estimation, and that the first derivative reflectances at 759 nm, 1954 nm and 2370 nm were most suitable to develop the estimation models of N, EE and CF concentrations, respectively. In addition, the high correlation coefficients of the theoretical and the measured biochemical parameters were obtained, especially for nitrogen (r=0.948).

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Section: Introductionmentioning

confidence: 99%

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Qian

Huang

Wang

et al. 2008

J. Zhejiang Univ. Sci. B

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“…An alternative method to predict yield variability is to use remote sensing. Many studies have been conducted to correlate remotely sensed data in the form of vegetation indices with the yield of various crops (Yang and Anderson 1996, Taylor et al, 1997, Zhang et al, 1998, Diker et al, 2001, Diker and Bausch 2003.…”

Section: Issn: 2319-7706 Volume 6 Number 10 (2017) Pp 1296-1307mentioning

confidence: 99%

Yield Prediction by Integrating NDVI and N-Tester Data with Yield Monitor Data

Sanodiya¹,

Singh²,

Bector³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

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“…In numerous studies, both HSI and MSI have proved their efficiency in assessing physiological parameters and nutrient levels in crop plants. Hinzman et al (1986), Takebe et al (1990), McMurtrey et al (1994), and Diker and Bausch (2003) studied the nitrogen effect on wheat, rice, and corn, respectively, using spectral reflectance. In addition, Casanova et al (1998) and Hansen and Schjoerring (2003) conducted experiments to determine vegetation indices for rice and wheat fields using HSI.…”

Section: Chlorophyll Pigments and Nitrogen Status Of Crop Plantsmentioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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Potential Use of Nitrogen Reflectance Index to estimate Plant Parameters and Yield of Maize

Cited by 47 publications

References 12 publications

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Quantifying biochemical variables of corn by hyperspectral reflectance at leaf scale

Yield Prediction by Integrating NDVI and N-Tester Data with Yield Monitor Data

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

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