Feasibility of near-infrared hyperspectral imaging to differentiate Canadian wheat classes

Sivakumar, Mahesh; Manickavasagan, Annamalai; Jayas, Digvir S.; Paliwal, Jitendra; White, N. D. G.

doi:10.1016/j.biosystemseng.2008.05.017

Cited by 164 publications

(89 citation statements)

References 18 publications

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“…Typically, studies that used handheld devices were conducted in the visible (350-700 nm) and near-infrared (NIR) portions of the electromagnetic spectrum (EMS). A number of studies have focused on discriminating weed species [5,7], different strands or cultivars of the same crop such as wheat, rice and cotton [6,23,24] and finally different species of crop altogether [8,22,25].…”

Section: Introductionmentioning

confidence: 99%

Separating Crop Species in Northeastern Ontario Using Hyperspectral Data

2014

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Abstract:The purpose of this study was to examine the capability of hyperspectral narrow wavebands within the 400-900 nm range for distinguishing five cash crops commonly grown in Northeastern Ontario, Canada. Data were collected from ten different fields in the West Nipissing agricultural zone (46°24'N lat., 80°07'W long.) and included two of each of the following crop types; soybean (Glycine max), canola (Brassica napus L.), wheat (Triticum spp.), oat (Avena sativa), and barley (Hordeum vulgare). Stepwise discriminant analysis was used to assess the spectral separability of the various crop types under two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that select hyperspectral bands in the visual and near infrared (NIR) regions (400-900 nm) can be used to effectively distinguish the five crop species under investigation. These bands, which were used in a variety of combinations include B465, B485, B495, B515, B525, B535, B545, B625, B645, B665, B675, B695, B705, B715, B725, B735, B745, B755, B765, B815, B825, B885, and B895. In addition, although species classification could be achieved at any point during the growing season, the optimal time for satellite image acquisition was determined to be in late July or approximately 75-79 days after planting with the optimal wavebands located in the red-edge, green, and NIR regions of the spectrum.

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Section: Introductionmentioning

confidence: 99%

Separating Crop Species in Northeastern Ontario Using Hyperspectral Data

2014

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“…The contribution level of each histogram and texture features were identified from the values of partial R2 and Wilks' Lambda and the top five most contributing features were identified and ranked for each classification models (Mahesh et al, 2008). The first step is done only to the most contributed variable on the classification and then the second variable is added in the following step.…”

Section: Classification Using Top Five Featuresmentioning

confidence: 99%

RGB color imaging to detect Aspergillus flavus infection in dates

Teena

Manickavasagan

Al-Sadi³

et al. 2016

Emir. J. Food Agric

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“…Canadian wheat classes has been determined by using near-infrared hyperspectral imaging (NIR-HSI) system [33]. Seventy-five relative reflectance intensities were extracted from the scanned images and used for the differentiation of wheat classes using a statistical classifier and an artificial neural network (ANN) classifier.…”

Section: Near-infrared Spectroscopymentioning

confidence: 99%