Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

Soares, Sófacles Figueredo Carreiro; Medeiros, Everaldo S.; Pasquini, Célio; Morello, C. de L.; Galvão, Roberto Kawakami Harrop; Araújo, Mário César Ugulino de

doi:10.1039/c6ay02896a

Cited by 34 publications

(15 citation statements)

References 34 publications

(29 reference statements)

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“…NIR-HSI has been used to classify the variety of seeds. [21][22][23] For maize seeds, Williams et al 24 used NIR-HSI to classify maize kernels of three hardness categories: hard, medium and so. Yang et al 25 classied four varieties of waxy corn seeds.…”

Section: Introductionmentioning

confidence: 99%

Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

et al. 2020

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“…Hyperspectral imaging (HSI) is an emerging technique that combines machine vision and spectroscopy systems and is becoming widely used in the variety classification of wheat [3,4], rice [5,6], cotton [7], and grape [8] seeds due to its decreased time requirements and material consumption benefits compared with traditional morphology or molecular identification methods [9,10]. In addition, the application of HSI in the variety classification of maize seed kernels also exhibits good performance [1,11,12,13,14], and even the near-infrared bands of the spectral imaging technique can be used to achieve good variety classification results of coated maize seeds [15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

Miao

Zhuang

Tang

et al. 2018

Sensors

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Variety classification is an important step in seed quality testing. This study introduces t-distributed stochastic neighbourhood embedding (t-SNE), a manifold learning algorithm, into the field of hyperspectral imaging (HSI) and proposes a method for classifying seed varieties. Images of 800 maize kernels of eight varieties (100 kernels per variety, 50 kernels for each side of the seed) were imaged in the visible- near infrared (386.7–1016.7 nm) wavelength range. The images were pre-processed by Procrustes analysis (PA) to improve the classification accuracy, and then these data were reduced to low-dimensional space using t-SNE. Finally, Fisher’s discriminant analysis (FDA) was used for classification of the low-dimensional data. To compare the effect of t-SNE, principal component analysis (PCA), kernel principal component analysis (KPCA) and locally linear embedding (LLE) were used as comparative methods in this study, and the results demonstrated that the t-SNE model with PA pre-processing has obtained better classification results. The highest classification accuracy of the t-SNE model was up to 97.5%, which was much more satisfactory than the results of the other models (up to 75% for PCA, 85% for KPCA, 76.25% for LLE). The overall results indicated that the t-SNE model with PA pre-processing can be used for variety classification of waxy maize seeds and be considered as a new method for hyperspectral image analysis.

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“…It can be seen from Figure 4 that the spectral trends of the four types of wheat kernels are very similar and are not well distinguished; however, in the range of 1400– 1650 nm, the difference in reflectance of the four types of wheat grains is the most obvious, and the order of spectral reflectance values from high to low is: Untreated > 1:1000 > 1:500 > 1:100. This is due to the second overtone of combination C–H and the second overtone of N–H in the range of 1400–1650 nm [26], which is related to the absorption of omethoate. Overall, the average spectral curve of wheat grain with a 1:100 concentration of pesticides differed most from healthy wheat grain (no pesticides); however, the difference between the average spectral curve of healthy wheat grain and wheat grain with a 1:500 and 1:1000 concentration of pesticide is very small.…”

Section: Resultsmentioning

confidence: 99%

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

Zhang

Rao

2019

Sensors

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In this study, a hyperspectral imaging system of 866.4–1701.0 nm was selected and combined with multivariate methods to identify wheat kernels with different concentrations of omethoate on the surface. In order to obtain the optimal model combination, three preprocessing methods (standard normal variate (SNV), Savitzky–Golay first derivative (SG1), and multivariate scatter correction (MSC)), three feature extraction algorithms (successive projections algorithm (SPA), random frog (RF), and neighborhood component analysis (NCA)), and three classifier models (decision tree (DT), k-nearest neighbor (KNN), and support vector machine (SVM)) were applied to make a comparison. Firstly, based on the full wavelengths modeling analysis, it was found that the spectral data after MSC processing performed best in the three classifier models. Secondly, three feature extraction algorithms were used to extract the feature wavelength of MSC processed data and based on feature wavelengths modeling analysis. As a result, the MSC–NCA–SVM model performed best and was selected as the best model. Finally, in order to verify the reliability of the selected model, the hyperspectral image was substituted into the MSC–NCA–SVM model and the object-wise method was used to visualize the image classification. The overall classification accuracy of the four types of wheat kernels reached 98.75%, which indicates that the selected model is reliable.

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Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

Abstract: This paper proposes the use of Near Infrared Hyperspectral Imaging (NIR-HSI) as a new strategy for fast and non-destructive classification of cotton seeds with respect to variety.

Cited by 34 publications

References 34 publications

Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds

Hyperspectral Image-Based Variety Classification of Waxy Maize Seeds by the t-SNE Model and Procrustes Analysis

NIR Hyperspectral Imaging Technology Combined with Multivariate Methods to Study the Residues of Different Concentrations of Omethoate on Wheat Grain Surface

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