Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat

Vigneau, Nathalie; Ecarnot, Martin; Rabatel, Gilles; Roumet, Pierre

doi:10.1016/j.fcr.2011.02.003

Cited by 204 publications

(142 citation statements)

References 25 publications

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“…Additional disadvantages of statistical models include the facts that they require a set of in situ data and that their robustness depends on the properties of these datasets (i.e., the number, quality and representativeness of the available reference samples), especially when extrapolated to other varieties, ecological sites, and growth stages [10,12,[15][16][17]20,25]. However, statistical models offer certain advantages that promote their widespread use.…”

Section: The Applicability Of the Six Algorithms To Different Groups mentioning

confidence: 99%

“…Existing reports indicate that in most previous work, the core wavelengths have first been determined and then used to construct a sensitive spectral index, as in the case of the continuum removal (CR) and the vegetation index (VI) method. The CR method can be used to effectively isolate individual absorption features of interest and estimate the chemical concentration in dried leaves [20][21][22]. However, one must determine the spectral range each time when the CR operation is performed, which results in unstable performance in monitoring of the chemical concentration of crops [23].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

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Abstract:The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation OPEN ACCESS Remote Sens. 2015, 7 14940 accuracies using the six metrics that were the coefficients of determination for the calibration (R 2 C) and validation (R 2 V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R² C, R² V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R² C, R² V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings...

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Section: The Applicability Of the Six Algorithms To Different Groups mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

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“…Franz et al (1991) reported that accurate spectral discrimination is possible for controlled leaf orientations, but discrimination accuracy significantly decreases as soon as leaf orientation changes. Vigneau et al (2011) explored the effect of leaf orientation for building a predictive model for nitrogen content using HSI. Identifying weeds in the crop rows is a much more difficult problem, which requires more advanced processing of spectral data (Brown et al, 2005;Slaughter et al, 2008;Hadoux et al, 2012).…”

Section: Limitationsmentioning

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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“…Diversos trabalhos têm buscado compreender a influência do nitrogênio no comportamento espectral ou determinar modelos específicos em diferentes culturas (OSBORNE et al, 2002;SMITH et al, 2003;ZHAO et al, 2005;JAIN et al, 2007;TIAN et al, 2011;VIGNEAU et al, 2011;ISMAIL, 2013). abaixo da média, média e acima da média.…”

Section: Utilização De Sensores Remotos No Monitoramento Do Nitrogêniounclassified

“…Em trabalho realizado por Vigneau et al (2011) VAN DEN BERG, 2010;MIPHOKASAP et al, 2012;AMARAL et al, 2015;ROSA et al, 2015).…”

Section: Introductionunclassified

Dados hiperespectrais de dossel e sua correlação com nitrogênio aplicado a cultura da cana-de-açúcar

Barros¹

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Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in Wheat

Cited by 204 publications

References 25 publications

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

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

Dados hiperespectrais de dossel e sua correlação com nitrogênio aplicado a cultura da cana-de-açúcar

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