Glyphosate‐resistant and glyphosate‐susceptible Palmer amaranth (<i>Amaranthus palmeri</i> S. Wats.): hyperspectral reflectance properties of plants and potential for classification

Reddy, Krishna N.; Huang, Yanbo; Lee, Matthew A.; Nandula, Vijay K.; Fletcher, Reginald S.; Thomson, Steven J.; Zhao, Feng

doi:10.1002/ps.3755

Cited by 36 publications

(27 citation statements)

References 13 publications

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“…15,22 Among different biotypes of the same weed species, however, varying levels of enzymes [for instance, overproduction of the 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase enzyme endowing resistance to glyphosate in glyphosate-resistant kochia and Palmer amaranth 20,23 ] produced by individuals (glyphosate-resistant versus susceptible biotype) can produce spectra that can be discriminated from one another.…”

Section: Image Processingmentioning

confidence: 99%

“…biotypes can be distinguished using differences in reflectance across the near UV, visible, and near IR spectrum in laboratory conditions. 15 Further work using hyperspectral reflectance profiles has been done to predict the herbicide resistance of Palmer amaranth biotypes, 16 differentiate between glyphosate-susceptible and glyphosate-resistant Italian ryegrass, 17 and to detect the injury on crops as a result of dicamba and glyphosate. 18 Weeds also have been distinguished from crops using shape rather than spectral data, 19 but in our application there are no systematic shape differences between herbicide-resistant and herbicide-susceptible kochia plants.…”

Section: Introductionmentioning

confidence: 99%

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Discrimination of herbicide-resistant kochia with hyperspectral imaging

Nugent

Shaw

Jha

et al. 2018

J. Appl. Rem. Sens.

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Abstract. A hyperspectral imager was used to differentiate herbicide-resistant versus herbicidesusceptible biotypes of the agronomic weed kochia, in different crops in the field at the Southern Agricultural Research Center in Huntley, Montana. Controlled greenhouse experiments showed that enough information was captured by the imager to classify plants as either a crop, herbicidesusceptible or herbicide-resistant kochia. The current analysis is developing an algorithm that will work in more uncontrolled outdoor situations. In overcast conditions, the algorithm correctly identified dicamba-resistant kochia, glyphosate-resistant kochia, and glyphosate-and dicamba-susceptible kochia with 67%, 76%, and 80% success rates, respectively. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Discrimination of herbicide-resistant kochia with hyperspectral imaging

Nugent

Shaw

Jha

et al. 2018

J. Appl. Rem. Sens.

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“…could be differentiated from GS Palmer amaranth using hyperspectral technology. In a series of experiments with three different populations (two grown in a greenhouse, and one in the field), it was consistently demonstrated that GS and GR Palmer amaranth could be differentiated better than 94% of the time without the use of a glyphosate application [5].…”

Section: Introductionmentioning

confidence: 94%

Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery

et al. 2014

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Glyphosate based herbicide programs are most preferred in current row crop weed control practices. With the increased use of glyphosate, weeds, including Italian ryegrass (Lolium multiflorum), have developed resistance to glyphosate. The identification of glyphosate resistant weeds in crop fields is critical because they must be controlled before they reduce the crop yield. Conventionally, the method for the identification with whole plant or leaf segment/disc shikimate assays is tedious and labor-intensive. In this research, we investigated the use of high spatial resolution hyperspectral imagery to extract spectral curves derived from the whole plant of Italian ryegrass to determine if the plant is glyphosate resistant (GR) or glyphosate sensitive (GS), which provides a way for rapid, non-contact measurement for differentiation between GR and GS weeds for effective site-specific weed management. The data set consists of 226 greenhouse grown plants (119 GR, 107 GS), which were imaged at three and four weeks after emergence. In image preprocessing, the spectral curves are normalized to remove lighting artifacts caused by height variation in the plants. In image analysis, a subset of hyperspectral bands is chosen using a forward selection algorithm to optimize the area under the receiver operating characteristic (ROC) between GR and GS plants. Then, the dimensionality of selected bands is reduced using linear discriminant analysis (LDA). Finally, the maximum likelihood classification was conducted for plant sample differentiation. The results show that the overall classification accuracy is between 75% and 80% depending on the age of the plants. Further refinement of the described methodology is needed to correlate better with plant age.

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“…Diminishing results are caused when there are not enough training samples to estimate joint probability distributions in high dimensional feature spaces [10]. In our previous research for differentiating GR and GS Palmer amaranth, the forward selection algorithm was conducted for spectral band selection [9]. The band selection process began with an empty set of selected bands.…”

Section: Band Selectionmentioning

confidence: 99%

“…In order to develop a rapid, non-destructive method for differentiating GR and GS weeds, Reddy et al [9] conducted a pilot study to differentiate GR Palmer amaranth (Amaranthus palmeri S. Wats.)…”

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confidence: 99%

Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass

Huang¹,

Lee²,

Nandula³

et al. 2018

AJPS

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Glyphosate is widely used in row crop weed control programs of glyphosate-resistant (GR) crops. With the accumulation of glyphosate use, several weeds have evolved resistance to glyphosate. In order to control GR weeds for profitable crop production, it is critical to first identify them in crop fields. Conventional method for identifying GR weeds is destructive, tedious and labor-intensive. This study developed hyperspectral imaging for rapid sensing of Italian ryegrass (Lolium perenne ssp. multiflorum) plants to determine if each plant is GR or glyphosate-susceptible (GS). In image analysis, a set of sensitive spectral bands was determined using a forward selection algorithm by optimizing the area under the receiver operating characteristic between GR and GS plants. Then, the dimensionality of selected bands was reduced using linear discriminant analysis. At the end the maximum likelihood classification was conducted for plant sample differentiation of GR Italian ryegrass from GS ones. The results indicated that the overall classification accuracy is between 75% and 80%. Although the accuracy is lower than the classification of Palmer amaranth (Amaranthus palmeri S. Wats.) in our previous study, this study provides a rapid, non-destructive approach to differentiate between GR and GS Italian ryegrass for improved site-specific weed management.

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Glyphosate‐resistant and glyphosate‐susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance properties of plants and potential for classification

Cited by 36 publications

References 13 publications

Discrimination of herbicide-resistant kochia with hyperspectral imaging

Discrimination of herbicide-resistant kochia with hyperspectral imaging

Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery

Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass

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