High speed measurement of corn seed viability using hyperspectral imaging

Ambrose, Ashabahebwa; Kandpal, Lalit Mohan; Kim, Moon S.; Lee, Wang‐Hee; Cho, Byoung‐Kwan

doi:10.1016/j.infrared.2015.12.008

Cited by 117 publications

(63 citation statements)

References 17 publications

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“…A positive band was observed at 2294 nm, which was an amino acid combination mode of NH and CO stretching at this wavelength . Moreover, the absorbance at the peak at 2275 nm corresponded to the OH/CH stretching related to cellulose . The peak at 2100 nm and the band at the 2280–2330 nm region related to carbohydrates and represents the combination of OH bending and CO stretching between the OH/CO polymers (OH and CO).…”

Section: Resultsmentioning

confidence: 89%

“…) provide information about the organic components responsible for the seed viability. For instance, the peaks around 1400–1450 nm correspond to the OH stretching of the 1 st overtone and are related to the NH 1 st overtone of the CONH 2 amine group . The peaks around 1896–1926 nm are related to starches and proteins due to absorption by a combination of the OH stretching vibration and CO stretching second overtone.…”

Section: Resultsmentioning

confidence: 99%

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Non‐destructive technique for determining the viability of soybean (Glycine max) seeds using FT‐NIR spectroscopy

et al. 2017

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The results demonstrated that the FT-NIR spectral analysis with the PLS-DA method that uses all variables or the selected variables showed good performance based on the high value of prediction accuracy for soybean viability with an accuracy close to 100%. Hence, FT-NIR techniques with a chemometric analysis have the potential for rapidly measuring soybean seed viability. © 2017 Society of Chemical Industry.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Non‐destructive technique for determining the viability of soybean (Glycine max) seeds using FT‐NIR spectroscopy

et al. 2017

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“…To reduce the interference of noise caused by uneven distribution of light source and dark current noise (AMBROSE et al, 2016), the system was calibrated before the experiment. The correction formula is as follows:…”

Section: Determination Of Leaf Spectral Datamentioning

confidence: 99%

Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network

Wang

et al. 2020

Cienc. Rural

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Chlorophyll is a major factor affecting photosynthesis; and consequently, crop growth and yield. In this study, we devised a chlorophyll-content detection model for millet leaves in different stages of growth based on hyperspectral data. The hyperspectral images of millet leaves were obtained under a wavelength range of 380-1000 nm using a hyperspectral imager. Threshold segmentation was performed with near-infrared (NIR) reflectance and normalized difference vegetation index (NDVI) to intelligently acquire the regions of interest (ROI). Furthermore, raw spectral data were preprocessed using multivariate scatter correction (MSC). A correlation coefficient-successive projections algorithm (CC-SPA) was used to extract the characteristic wavelengths, and the characteristic parameters were extracted based on the spectral and image information. A partial least squares regression (PLSR) prediction model was established based on the single characteristic parameter and multi-characteristic parameter fusion. The determination coefficient (Rv 2) and the root-mean-square error (RMSEv) of the validation set for the multi-characteristic parameter fusion model were reported to be 0.813 and 1.766, respectively, which are higher than those obtained by the single characteristic parameter model. Based on the multi-characteristic parameter fusion, an attention-convolutional neural network (attention-CNN) (Rv 2 = 0.839, RMSEv = 1.451, RPD = 2.355) was established, which is more effective than the PLSR (Rv 2 = 0.813, RMSEv = 1.766, RPD = 2.167) and least squares support vector machine (LS-SVM) models (Rv 2 = 0.806, RMSEv = 1.576, RPD = 2.061). These results indicated that the combination of hyperspectral imaging and attention-CNN is beneficial to the application of nutrient element monitoring of crops.

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“…In recent years, sparse representation theory [7] has been used more and more in image processing, including image restoration, image denoising, image recognition etc. mainly.…”

Section: Sparse Representationmentioning

confidence: 99%

“…In the domestic research, progress has been made in the detection and identification of maize varieties using machine vision technology. Grains collecting and processing are carried out by machine vision, characteristic parameters of maize are chosen and optimized [4][5][6][7][8][9] by a series of algorithm such as artificial neural network model, support vector machines etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-kernel dictionary learning for maize varieties classification

Zhu¹,

Yue²,

Li³

et al. 2018

International Journal of Agricultural and Biological Engineering

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The automatic classification and identification of maize varieties is one of the important research contents in agriculture. A multi-kernel maize varieties classification approach was proposed in this paper in order to improve the recognition rate of maize varieties. In this approach, four kinds of maize varieties were selected, in each variety 200 grains were selected randomly as the samples, and in each sample 160 grains were taken as the training samples randomly; the characteristics of maize grain were extracted as the typical characteristics to distinguish maize varieties, by which the dictionary required by K-SVD was constructed; for the test samples, the feature-matrixes were extracted by dimension reduction method which were mapped to the high-dimension space by muti-kernel function mapping. The high-dimension characteristic matrixes were trained by K-SVD method and the corresponding feature dictionary was obtained respectively. Finally, the test samples representing were trained and classified by l 2,1 minimization sparse coefficient. The experiment results showed that recognition rate was improved obviously through this approach, and the poor-effect to maize variety identification from partial occlusion can be eliminated effectively.

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High speed measurement of corn seed viability using hyperspectral imaging

Cited by 117 publications

References 17 publications

Non‐destructive technique for determining the viability of soybean (Glycine max) seeds using FT‐NIR spectroscopy

Non‐destructive technique for determining the viability of soybean (Glycine max) seeds using FT‐NIR spectroscopy

Chlorophyll content for millet leaf using hyperspectral imaging and an attention-convolutional neural network

Multi-kernel dictionary learning for maize varieties classification

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