Brown Rice Germ Integrity Identification Based on Deep Learning Network

Li, Shuofeng; Li, Bing; Li, Jin; Liu, Bin

doi:10.1155/2022/6709787

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Deep learning has been widely used in foods and agriculture in recent years [7,8], and image recognition of plants has received a lot of attention from researchers. Following this trend, nondestructive age recognition of the tangerine peel contributes to the development of the intelligent tangerine peel industry.…”

Section: Introductionmentioning

confidence: 99%

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

Deng,

Li,

et al. 2024

Journal of Food Quality

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Xinhui tangerine peel has valuable medicinal value. The longer it is stored in an appropriate environment, the higher its flavonoid content, resulting in increased medicinal value. In order to correctly identify the age of the tangerine peel, previous studies have mostly used manual identification or physical and chemical analysis, which is a tedious and costly process. This work investigates the automatic age recognition of the tangerine peel based on deep learning and attention mechanisms. We proposed an effective ConvNeXt fusion attention module (CNFA), which consists of three parts, a ConvNeXt block for extracting low-level features’ information and aggregating hierarchical features, a channel squeeze-and-excitation (cSE) block and a spatial squeeze-and-excitation (sSE) block for generating sufficient high-level feature information from both channel and spatial dimensions. To analyze the features of tangerine peel in different ages and evaluate the performance of CNFA module, we conducted comparative experiments using the CNFA-integrated network on the Xinhui tangerine peel dataset. The proposed algorithm is compared with related models of the proposed structure and other attention mechanisms. The experimental results showed that the proposed algorithm had an accuracy of 97.17%, precision of 96.18%, recall of 96.09%, and F1 score of 96.13% for age recognition of the tangerine peel, providing a visual solution for the intelligent development of the tangerine peel industry.

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Section: Introductionmentioning

confidence: 99%

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

Deng,

Li,

et al. 2024

Journal of Food Quality

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“…The automatic identification method of pipeline weld defects based on deep learning [2][3][4][5][6][7][8][9][10][11][12] can effectively solve the problems of time-consuming labor and low efficiency of manual film evaluation. Liu Han et al [13] used convolutional neural network to automatically extract deep-level features of defects, which not only realized automatic classification, but also improved the time-consuming and laborious problem of manual feature extraction; Jiang Hongquan et al [14] used random forest algorithm to improve the feature selection method of CNN model, and improved the parameter learning ability of traditional CNN method; Daniel Bacioiu et al [15] used the fully connected network architecture combined with convolutional neural network to identify 6 types of defects, 4 types of defects, and 2 types of defects, with accuracy rates of 70%, 85%, and 95%, respectively, it can be seen that the classification accuracy of the model decreases significantly in the face of multiple defects.…”

Section: Introductionmentioning

confidence: 99%

Weld defect recognition method based on improved DenseNet

Luo

Tang

et al. 2023

Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022)

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There are many subjective influencing factors, poor recognition effect and low efficiency in manual evaluation of pipeline weld defects. An intelligent identification method of pipeline weld defects based on improved DenseNet network is proposed. This method firstly uses the form of multi-channel convolution of different scales to improve the DenseNet network, thereby improving the generalization ability of the network. Then, the feature extraction ability of the network is improved by stacking two convolutions of the same scale. Finally, an attention mechanism module is introduced into the dense connection block of the network to achieve the effect of improving beneficial features and suppressing useless features. The experimental results show that the method can achieve 92% accuracy in the identification of pipeline weld defects, which is about 13% higher than the original method, and has high efficiency, which can fully achieve the purpose of industrial application.

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“…Li and Li [ 23 ] improved Inception-v3 by introducing fine-grained classification to learn local features of rice and to identify the integrity of the rice germ. Li et al [ 24 ] refined the Inception-v3 model to detect the integrity of the germ with the addition of mutual channel loss and mlpconv. Li et al [ 25 ] identified rice germ integrity based on the EfficientNet-B3 model with the introduction of the double attention network (DAN).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

Chen

Wang

2022

Foods

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Traditional machine learning-based methods for the detection of rice degree of milling (DOM) that are not comprehensive in feature extraction and have low recognition rates fail to meet the demand for fast, non-destructive, and accurate detection. This paper presents a digital image processing technology combined with deep learning to implement the classification of DOM of rice. An improved multi-scale information fusion model of the InceptionResNet–Bayesian optimization algorithm (IRBOA) was constructed based on the Inception-v3 structure and residual network (ResNet) model. It enables to automatically extract more comprehensive features of rice and determine the DOM of rice. Additionally, the important hyperparameters in the model were tuned by the BOA to optimize the recognition rate of rice DOM. The results show the hyperparameters optimized using the BOA are those that would not be chosen in manual tuning. The classification precision of the IRBOA model reached 99.22%, 94.92%, and 96.55% for well-milled, reasonably well-milled, and substandard rice, respectively, with an average accuracy of no less than 96.90%. This model improved 7.41% over the traditional machine learning model and at least 1.35% over the fashionable CNN model with strong generalization performance. This method effectively completes rapid, non-destructive, and accurate intelligent detection of rice DOM, which can supply a reliable and accurate technical mean for rice processing enterprises to guide the rice processing process.

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Brown Rice Germ Integrity Identification Based on Deep Learning Network

Cited by 7 publications

References 29 publications

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

CNFA: ConvNeXt Fusion Attention Module for Age Recognition of the Tangerine Peel

Weld defect recognition method based on improved DenseNet

Evaluation of Rice Degree of Milling Based on Bayesian Optimization and Multi-Scale Residual Model

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