Peach variety identification using near-infrared diffuse reflectance spectroscopy

Guo, Wenchuan; Gu, Jingsi; Liu, Dayang; Shang, Liang

doi:10.1016/j.compag.2016.03.005

Cited by 62 publications

(23 citation statements)

References 35 publications

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“…These exposures indicate that the wax on the sugarcane surface may influence the spectra of visible-shortwave near infrared. Therefore, the accuracy of spectra acquisition should be examined to design the sample preparation before modeling, such as by using the repeatability and reproducibility values [17] or principal component analysis [18]. The PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables [18].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the accuracy of spectra acquisition should be examined to design the sample preparation before modeling, such as by using the repeatability and reproducibility values [17] or principal component analysis [18]. The PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables [18]. This method was widely used to identify the cultivars of fruits and plants by detecting the differences among the cultivars such as European pear cultivars [19] and the peach variety [18].…”

Section: Introductionmentioning

confidence: 99%

“…The PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables [18]. This method was widely used to identify the cultivars of fruits and plants by detecting the differences among the cultivars such as European pear cultivars [19] and the peach variety [18]. Therefore, the PCA method was used to examine the effect of waxy types on the cane surface.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Phuphaphud¹,

Saengprachatanarug²,

Posom³

et al. 2019

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The precision of spectroscopic methods was frequently affected by the identity of the inhomogeneous materials, especially for direct scanning. This research aimed to investigate the effects of waxy types, naturally found on sugarcane surface, on spectral characteristics. A portable Vis/SWNIR instrument with interactance mode across wavelength of 570-1031 nm was used for direct scanning the cane stalk. Principal component analysis (PCA) was applied to examine the differences in the spectra scanned from 180 samples, including 3 types of waxy type: white, black, and mixed black and white. Seven widespread pretreatments were employed to reduce the effect of the waxy types. Results show that the spectra of the samples with each waxy type was separated in groups and standard normal variate (SNV) pretreatment gave the best results. However, it was not able to eliminate the effect compared to the wax-removed samples. Meanwhile, the standard deviation of absorbance values, at the wavelength of 760, 904 and 970 nm of 3 samples, was used for assessing the repeatability and reproducibility. The samples where the waxy covers were removed provided a lower standard deviation of absorbance values of spectra than the best pretreated spectra that used SNV of the samples that retained its waxy covers by one to six times. Thus, the waxy material on cane surface should be removed before collecting spectra.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Phuphaphud¹,

Saengprachatanarug²,

Posom³

et al. 2019

View full text Add to dashboard Cite

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“…Good results have been achieved for discriminating cultivars in permanent crops. A Fourier transform NIR spectrometer was used by Guo et al [90] to distinguish four peach varieties commonly used in China. Suphamitmongkol et al [91] also differentiated three varieties of Thai orange through the use of a short-wavelength spectrometer.…”

Section: Discussionmentioning

confidence: 99%

Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

et al. 2020

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Worldwide sustainable development is threatened by current agricultural land change trends, particularly by the increasing rural farmland abandonment and agricultural intensification phenomena. In Mediterranean countries, these processes are affecting especially traditional olive groves with enormous socio-economic costs to rural areas, endangering environmental sustainability and biodiversity. Traditional olive groves abandonment and intensification are clearly related to the reduction of olive oil production income, leading to reduced economic viability. Most promising strategies to boost traditional groves competitiveness—such as olive oil differentiation through adoption of protected denomination of origin labels and development of value-added olive products—rely on knowledge of the olive varieties and its specific properties that confer their uniqueness and authenticity. Given the lack of information about olive varieties on traditional groves, a feasible and inexpensive method of variety identification is required. We analyzed leaf spectral information of ten Portuguese olive varieties with a powerful data-mining approach in order to verify the ability of satellite’s hyperspectral sensors to provide an accurate olive variety identification. Our results show that these olive varieties are distinguishable by leaf reflectance information and suggest that even satellite open-source data could be used to map them. Additional advantages of olive varieties mapping were further discussed.

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“…The effectiveness of visible and near-infrared reflectance spectroscopy to non-destructively discriminate crop varieties was achieved in several species, such as wheat [4][5][6], Chinese bayberry [7], peach varieties [8], and Thai tangerine varieties [9].…”

Section: Introductionmentioning

confidence: 99%

Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

et al. 2019

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The application of spectral sensors mounted on unmanned aerial vehicles (UAVs) assures high spatial and temporal resolutions. This research focused on canopy reflectance for cultivar recognition in an olive grove. The ability in cultivar recognition of 14 vegetation indices (VIs) calculated from reflectance patterns (green520–600, red630–690 and near-infrared760–900 bands) and an image segmentation process was evaluated on an open-field olive grove with 10 different scion/rootstock combinations (two scions by five rootstocks). Univariate (ANOVA) and multivariate (principal components analysis—PCA and linear discriminant analysis—LDA) statistical approaches were applied. The efficacy of VIs in scion recognition emerged clearly from all the approaches applied, whereas discrimination between rootstocks appeared unclear. The results of LDA ascertained the efficacy of VI application to discriminate between scions with an accuracy of 90.9%, whereas recognition of rootstocks failed in more than 68.2% of cases.

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Peach variety identification using near-infrared diffuse reflectance spectroscopy

Cited by 62 publications

References 35 publications

Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Effects of Waxy Types of a Sugarcane Stalk Surface on the Spectral Characteristics of Visible-Shortwave Near Infrared Measurement

Hyperspectral Reflectance as a Basis to Discriminate Olive Varieties—A Tool for Sustainable Crop Management

Remotely Sensed Vegetation Indices to Discriminate Field-Grown Olive Cultivars

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