A Band Influence Algorithm for Hyperspectral Band Selection to Classify Moldy Peanuts

Liu, Ziwei; Jiang, Jinbao; Du, Yanjun; Xu, Zhifang

doi:10.1109/access.2021.3123172

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…Some scholars have used HSI to discriminate and assess the quality and safety of diverse foods, and obtained satisfactory results, such as defect detection of citrus [24], classification of mildew, health, and damage of peanuts [25], prediction of potential pest infection of apples [26], and identification of Cucumber Green Mottle Mosaic Virus (CGMMV) infection in watermelon seeds [27]. HSI technology has also been extensively employed in the study of pesticide residues.…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging

Wang

et al. 2023

Foods

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In the field of safety detection of fruits and vegetables, how to conduct non-destructive detection of pesticide residues is still a pressing problem to be solved. In response to the high cost and destructive nature of existing chemical detection methods, this study explored the potential of identifying different pesticide residues on Hami melon by short-wave infrared (SWIR) (spectral range of 1000–2500 nm) hyperspectral imaging (HSI) technology combined with machine learning. Firstly, the classification effects of classical classification models, namely extreme learning machine (ELM), support vector machine (SVM), and partial least squares discriminant analysis (PLS-DA) on pesticide residues on Hami melon were compared, ELM was selected as the benchmark model for subsequent optimization. Then, the effects of different preprocessing treatments on ELM were compared and analyzed to determine the most suitable spectral preprocessing treatment. The ELM model optimized by Honey Badger Algorithm (HBA) with adaptive t-distribution mutation strategy (tHBA-ELM) was proposed to improve the detection accuracy for the detection of pesticide residues on Hami melon. The primitive HBA algorithm was optimized by using adaptive t-distribution, which improved the structure of the population and increased the convergence speed. Compared the classification results of tHBA-ELM with HBA-ELM and ELM model optimized by genetic algorithm (GA-ELM), the tHBA-ELM model can accurately identify whether there were pesticide residues and different types of pesticides. The accuracy, precision, sensitivity, and F1-score of the test set was 93.50%, 93.73%, 93.50%, and 0.9355, respectively. Metaheuristic optimization algorithms can improve the classification performance of classical machine learning classification models. Among all the models, the performance of tHBA-ELM was satisfactory. The results indicated that SWIR-HSI coupled with tHBA-ELM can be used for the non-destructive detection of pesticide residues on Hami melon, which provided the theoretical basis and technical reference for the detection of pesticide residues in other fruits and vegetables.

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Section: Introductionmentioning

confidence: 99%

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging

Wang

et al. 2023

Foods

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“…Aiming at the problems mentioned above, this study proposes a lightweight neuralnetwork-based method for recognizing edible fungi diseases in the fruit body period. This paper improves the ShuffleNetV2 network based on the fusion of an attention mechanism and obtains the ShufflenetV2-Lite+SE model [42][43][44][45][46][47], which is used to recognize edible fungi diseases in the fruit body period. This method provides certain technical guidance for the recognition of edible fungi diseases.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

et al. 2023

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Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi.

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“…Hyperspectral technology is often used for rapid non-destructive testing of agricultural products [6][7][8]. In previous studies, researchers have used hyperspectral technology to identify moldy peanuts [9][10][11][12]. For example, He et al used visible-near infrared hyperspectral images to classify 150 peanuts naturally polluted by aflatoxin B1 at the particle level, and achieved a classification accuracy of 94% on the support vector machines (SVM) classifier [9].…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al used 400-1000 nm hyperspectral images to classify 2171 peanuts and constructed a band selection model for feature selection to identify healthy, moldy, and damaged peanuts. The classification accuracy of 97.66% was achieved when 10 feature bands were used in the ShuffleNet V2 model [10]. Therefore, it is feasible to carry out the identification of moldy peanuts based on hyperspectral technology.…”

Section: Introductionmentioning

confidence: 99%

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

Liu

Jiang

et al. 2022

Foods

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Aflatoxins in moldy peanuts are seriously toxic to humans. These kernels need to be screened in the production process. Hyperspectral imaging techniques can be used to identify moldy peanuts. However, the changes in spectral information and texture information caused by the difference in moisture content in peanuts will affect the identification accuracy. To reduce and eliminate the influence of this factor, a data augmentation method based on interpolation was proposed to improve the generalization ability and robustness of the model. Firstly, the near-infrared hyperspectral images of 5 varieties, 4 classes, and 3 moisture content gradients with 39,119 kernels were collected. Then, the data augmentation method called the difference of spectral mean (DSM) was constructed. K-nearest neighbors (KNN), support vector machines (SVM), and MobileViT-xs models were used to verify the effectiveness of the data augmentation method on data with two gradients and three gradients. The experimental results show that the data augmentation can effectively reduce the influence of the difference in moisture content on the model identification accuracy. The DSM method has the highest accuracy improvement in 5 varieties of peanut datasets. In particular, the accuracy of KNN, SVM, and MobileViT-xs using the data of two gradients was improved by 3.55%, 4.42%, and 5.9%, respectively. Furthermore, this study provides a new method for improving the identification accuracy of moldy peanuts and also provides a reference basis for the screening of related foods such as corn, orange, and mango.

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A Band Influence Algorithm for Hyperspectral Band Selection to Classify Moldy Peanuts

Cited by 7 publications

References 38 publications

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging

Non-Destructive Detection of Different Pesticide Residues on the Surface of Hami Melon Classification Based on tHBA-ELM Algorithm and SWIR Hyperspectral Imaging

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

Identification of Moldy Peanuts under Different Varieties and Moisture Content Using Hyperspectral Imaging and Data Augmentation Technologies

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