Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts

Qiao, Xuejun; Jiang, Jinbao; Qi, Xiaotong; Guo, Haitao; Yuan, Deshuai

doi:10.1016/j.foodchem.2016.09.119

Cited by 56 publications

(28 citation statements)

References 22 publications

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“…The main purpose of this work was to study classification algorithms of hyperspectral image pixels and analyse the performance of deep models for diagnosing Fusarium head blight disease in wheat. Previous studies attempted to classify the disease symptoms based on spatial and spectral features via machine learning algorithms [14,15]; for example, PCA [33][34][35], random forest, and SVM [17][18][19]. Recently, the study of deep algorithms for hyperspectral imagery has becomes increasingly intensive, such as DCNN [24][25][26][27], DRNN [31], and hybrid neural networks [32].…”

Section: Discussionmentioning

confidence: 99%

“…The traditional commonly used algorithm is a Support Vector Machine (SVM), which has achieved remarkable results in statistical process control applications [17]. Qiao uses the method of SVM to classify fungi-contaminated peanuts in hyperspectral image pixels, and the classification accuracy exceeded 90% [18]. When using SVM to classify hyperspectral images, the spectral and spatial features should be extracted with reduced dimensionality.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

et al. 2018

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Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN). After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU) has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

et al. 2018

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“…In crops like peanuts, near infrared wavelengths have been used to identify mycotoxigenic fungi with >94% classification accuracy [29]. This study with peanuts was conducted with grains completely colonised by the fungus.…”

Section: Introductionmentioning

confidence: 99%

Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

et al. 2017

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Aflatoxin-producing Aspergillus spp. produce carcinogenic metabolites that contaminate maize. Maize kernel absorbance patterns of near infrared (NIR) wavelengths (800-2600 nm) were used to non-invasively identify kernels of milk-, dough-and dent-stage maturities with four doses of Aspergillus sp. contamination. Near infrared spectrometry (NIRS) spectral data was pre-processed using first derivative Savitzky-Golay (1d-SG) transformation and multiplicative scatter correction on spectral data. Contaminated kernels had higher absorbance between 800-1134 nm, while uninoculated samples had higher absorbance above 1400 nm. Dose and maturity clusters seen in Principal Component Analysis (PCA) score plots were due to bond stretches of combination bands, CH and C=O functional groups within grain macromolecules. The regression model at 2198 nm separated uninoculated and inoculated kernels (p < 0.0001, R 2 = 0.88, root mean square error = 0.15). Non-invasive identification of Aspergillus-contaminated maize kernels using NIR spectrometry was demonstrated in kernels of different maturities.

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“…Analysis of variance (ANOVA) is commonly used to compare the difference between sample means under varying experimental conditions. One‐way ANOVA studies the influence of only a single factor on observed variables . F ‐values of one‐way ANOVA analysis between the flour and ADC samples were calculated, in order to determine the optimal band ratio for classifying the two samples.…”

Section: Methodsmentioning

confidence: 99%

“…One-way ANOVA studies the influence of only a single factor on observed variables. [36][37][38] F-values of one-way ANOVA analysis between the flour and ADC samples were calculated, in order to determine the optimal band ratio for classifying the two samples. Higher F-values indicate more prominent differences between the two samples.…”

Section: Data Processingmentioning

confidence: 99%

Near‐infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour

et al. 2017

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Utilization of spectral-spatial characteristics in shortwave infrared hyperspectral images to classify and identify fungi-contaminated peanuts

Cited by 56 publications

References 22 publications

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

Near Infrared Spectrometry for Rapid Non-Invasive Modelling of Aspergillus-Contaminated Maturing Kernels of Maize (Zea mays L.)

Near‐infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour

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