Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance

Rotbart, Nativ; Schmilovitch, Z.; Cohen, Yafit; Alchanatis, V.; Erel, Ran; Ignát, Tímea; Shenderey, C.; Dag, Arnon; Yermiyahu, Uri

doi:10.1016/j.biosystemseng.2012.09.005

Cited by 49 publications

(26 citation statements)

References 28 publications

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“…This study was performed to assess the feasibility of the Vis/NIR spectra for rapid N concentration prediction using pear leaves of two cultivars. The absorption features of the pigment and water were consistent with those of common crops (Curran, 1989; Rotbart et al, 2013; Alchanatis et al, 2005). The characteristic spectra of the two cultivars analyzed were presented the same tendency and no significant differences.…”

Section: Discussionsupporting

confidence: 63%

Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near‐Infrared Reflectance Spectroscopy

et al. 2014

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A rapid and reliable method is required to determine the N status of pear (Pyrus communis L.) leaves during the growing season for timely fertilization to improve the yields and fruit quality. In the present study, we evaluated visible and near-infrared re ectance (Vis/NIR) spectra of fresh pear leaves using partial least squares (PLS) regression to determine the N concentration of fresh pear leaves. In addition, we studied the performance of modi ed spectra generated using di erent preprocessing techniques. A total of 450 leaf samples were collected from 6-yr-old pear trees of two cultivars, and randomly separated into two subsets (calibration subset [294 samples] and validation subset [180 samples]) a er excluding outliers by using principle component analysis.Results showed that the model built using full spectra performed better than that developed using characteristic wavelength segments. In addition, we found that original spectral proved to provide better accuracy than derivative spectra. Among the studied preprocessing techniques, moving average smoothing (MAS) technique improved accuracy the most. Overall results suggested that PLS regression with preprocessing of full spectra using MAS is optimal method for modeling N concentration of fresh pear leaves which yielded 0.961 and 0.953 coe cient of determination (R 2 ) for calibration and cross-validation, respectively. e validation of this method resulted high R 2 value (0.847) and low mean relative error (4.48%). In conclusion, this model could provide a rapid and more reliable method to determine the total N concentration in fresh pear leaves and could be useful for fertilization management in pear orchards.

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Section: Discussionsupporting

confidence: 63%

Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near‐Infrared Reflectance Spectroscopy

et al. 2014

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“…Light reflected by vegetation in the VIS region of the spectrum (350–750 nm) in remote sensing experiments is predominantly influenced by chlorophyll pigments in the leaf tissues [39]. However, the short-wave infrared region was highly correlated with material absorption especially some structural leaf compounds, such as cell walls, cellulose, and lignin [40,41], leaf cellular structure, such as the volume of the air spaces, and the direction of incoming light [42]. Spectral measurement using the black background can minimize the influence of dust on the underside of leaves, and was found to be more related to the leaf nitrogen concentration.…”

Section: Discussionmentioning

confidence: 99%

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

Wang

Shen

Liu

et al. 2017

Sensors

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Non-destructive and timely determination of leaf nitrogen (N) concentration is urgently needed for N management in pear orchards. A two-year field experiment was conducted in a commercial pear orchard with five N application rates: 0 (N0), 165 (N1), 330 (N2), 660 (N3), and 990 (N4) kg·N·ha−1. The mid-portion leaves on the year’s shoot were selected for the spectral measurement first and then N concentration determination in the laboratory at 50 and 80 days after full bloom (DAB). Three methods of in-field spectral measurement (25° bare fibre under solar conditions, black background attached to plant probe, and white background attached to plant probe) were compared. We also investigated the modelling performances of four chemometric techniques (principal components regression, PCR; partial least squares regression, PLSR; stepwise multiple linear regression, SMLR; and back propagation neural network, BPNN) and three vegetation indices (difference spectral index, normalized difference spectral index, and ratio spectral index). Due to the low correlation of reflectance obtained by the 25° field of view method, all of the modelling was performed on two spectral datasets—both acquired by a plant probe. Results showed that the best modelling and prediction accuracy were found in the model established by PLSR and spectra measured with a black background. The randomly-separated subsets of calibration (n = 1000) and validation (n = 420) of this model resulted in high R2 values of 0.86 and 0.85, respectively, as well as a low mean relative error (<6%). Furthermore, a higher coefficient of determination between the leaf N concentration and fruit yield was found at 50 DAB samplings in both 2015 (R2 = 0.77) and 2014 (R2 = 0.59). Thus, the leaf N concentration was suggested to be determined at 50 DAB by visible/near-infrared spectroscopy and the threshold should be 24–27 g/kg.

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“…Rotbart et al compared the performance of three different NIR spectrometers, including portable instruments, in determining the nitrogen content of olive tree leaves. 137 Other applications include the differentiation of some plastics and foodstuffs using portable NIR spectroscopy instruments, relying not only on a statistical and mathematical approach but also on a chemical interpretation of the NIR spectra. Researchers intended to continue the basic interpretation of NIR spectra by focusing on various foodstuffs and, thus, to provide a useful analytical technique for many industrial needs.…”

Section: Applications In the Agro-food Industrymentioning

confidence: 99%

A Review on the Applications of Portable Near-Infrared Spectrometers in the Agro-Food Industry

et al. 2013

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Industry has created the need for a cost-effective and nondestructive quality-control analysis system. This requirement has increased interest in near-infrared (NIR) spectroscopy, leading to the development and marketing of handheld devices that enable new applications that can be implemented in situ. Portable NIR spectrometers are powerful instruments offering several advantages for nondestructive, online, or in situ analysis: small size, low cost, robustness, simplicity of analysis, sample user interface, portability, and ergonomic design. Several studies of on-site NIR applications are presented: characterization of internal and external parameters of fruits and vegetables; conservation state and fat content of meat and fish; distinguishing among and quality evaluation of beverages and dairy products; protein content of cereals; evaluation of grape ripeness in vineyards; and soil analysis. Chemometrics is an essential part of NIR spectroscopy manipulation because wavelength-dependent scattering effects, instrumental noise, ambient effects, and other sources of variability may complicate the spectra. As a consequence, it is difficult to assign specific absorption bands to specific functional groups. To achieve useful and meaningful results, multivariate statistical techniques (essentially involving regression techniques coupled with spectral preprocessing) are therefore required to extract the information hidden in the spectra. This work reviews the evolution of the use of portable near-infrared spectrometers in the agro-food industry.

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Estimating olive leaf nitrogen concentration using visible and near-infrared spectral reflectance

Cited by 49 publications

References 28 publications

Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near‐Infrared Reflectance Spectroscopy

Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near‐Infrared Reflectance Spectroscopy

Non-Destructive Evaluation of the Leaf Nitrogen Concentration by In-Field Visible/Near-Infrared Spectroscopy in Pear Orchards

A Review on the Applications of Portable Near-Infrared Spectrometers in the Agro-Food Industry

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