Detection of Wheat Unsound Kernels Based on Improved ResNet

Gao, Hui; Tong, Zhen; Li, Zhihui

doi:10.1109/access.2022.3147838

Cited by 13 publications

(4 citation statements)

References 35 publications

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“…Moreover, we confirmed that the CNN model is more accurate than the latest DenseNet model. Because the DenseNet model tends to extract similar features as the hierarchy deepens, which affects accuracy [52]. Our study shows that the CNN model is better than the DenseNet model in terms of the classification accuracy.…”

Section: B Classification Accuracies Obtained From M-densenet and C-d...mentioning

confidence: 84%

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Bak,

Park,

Lee

et al. 2024

IEEE Trans. Emerg. Top. Comput. Intell.

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Typical convolutional neural networks (CNNs) are widely used to recognize a user's stress state using the functional near-infrared spectroscopy (fNIRS), which is the latest brain imaging technology. fNIRS signals are usually fed into CNN models in the form of high-dimensional image data. However, this approach is not easy to achieve high classification accuracy because of physiological noises in brain signals. It is also likely to overlook the process of evaluating the reliability of calculated classification accuracy. To solve these problems, we proposed a memristor-based CNN (M-CNNs) This model's weight update process involves using stochastic gradient descent with momentum (SGDM), where the normalized conductances of memristors are used as weight substitutes. These conductances are then adjusted to classify stress states. We calculated the classification accuracies between the control and stress groups by using the M-CNNs, and then compared them with those of the CNNs. We used DenseNet, the most recent CNN model, to simulate accuracy under the same conditions. To ensure a fair comparison, we divided the DenseNet into the memristorbased DenseNet (M-DenseNet) and the conventional DenseNet (C-DenseNet). As a result, we discovered that the accuracy of M-CNNs (93.33%) exceeded that of CNNs (87.50%), and is reliable by precision, recall, and F-Score calculated from a confusion matrix. Likewise, M-DenseNet (92.38%) has higher accuracy than C-DenseNet (90.00%), but shows lower accuracy than M-CNNs. Moreover, we observed the reproducibility of M-CNN/DenseNet in various datasets. Therefore, our study suggests a promising application of CNN by combining conductances of memristor for classifying stress states.

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Section: B Classification Accuracies Obtained From M-densenet and C-d...mentioning

confidence: 84%

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Bak,

Park,

Lee

et al. 2024

IEEE Trans. Emerg. Top. Comput. Intell.

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“…In conclusion, researchers at home and abroad have undertaken a series of studies on the detection of agricultural products and achieved good results [18][19][20][21], which provided a reference for the detection of maize kernel quality. For example, Bi et al [22] combined deep learning with machine vision and used the basis of Swin Transformer to improve maize seed recognition.…”

Section: Introductionmentioning

confidence: 99%

Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

Yang,

Liu,

Wang

et al. 2024

Agriculture

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As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.

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“…At present, deep learning has been widely applied in agriculture. Convolutional neural network technology has been applied in the actual agricultural environment, such as image classification [26][27][28], object detection [29][30][31][32], and image segmentation [33][34][35][36][37], with good performance. Therefore, the study of peanut kernels and peanut pods in this field has attracted the attention of many scholars.…”

Section: Introductionmentioning

confidence: 99%

Method of Peanut Pod Quality Detection Based on Improved ResNet

Wang

et al. 2023

Agriculture

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Peanuts are prone to insect damage, breakage, germination, mildew, and other defects, which makes the quality of peanuts uneven. The difference in peanut pod quality makes the price and economic benefit also have a big difference. The classification of peanut pods according to quality is an important part of improving the product grade and market competitiveness. Real-time, accurate, and non-destructive quality detection of peanut pods can effectively improve the utilization and commercial value of peanuts. The strong subjectivity of manual detection and the low efficiency and low accuracy of mechanical detection have caused considerable wastage. Therefore, the present study proposed a new convolutional neural network for the peanut pod quality detection algorithm (PQDA) based on an improved ResNet. Compared to previous models, this model is more practical with high accuracy, lightweight, and easy nesting. Firstly, the detection and classification effects of ResNet18, AlexNet, and VGG16 are compared, and ResNet18 was determined to be the best backbone feature extraction network for model training. Secondly, three models were designed to optimize and improve the algorithm. The KRSNet module was added to the algorithm to make the model lightweight. The CSPNet module was added to the algorithm to improve the learning efficiency of each feature layer. The Convolutional Block Attention Module (CBAM) was added to the algorithm to improve its ability to capture more feature information about peanut pods. The experimental ablation results show that the precision of the improved model PQDA reaches 98.1%, and the size of parameters is only 32.63 M. Finally, the optimized model was applied to other peanut pod varieties for generalization experiments, and the accuracy reached 89.6% and 90.0%, indicating the effectiveness of the proposed peanut pod quality detection model. Furthermore, the model is suitable for deployment on embedded resource-limited devices, such as mobile terminals, to achieve the real-time and accurate detection of peanut pod quality.

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Detection of Wheat Unsound Kernels Based on Improved ResNet

Cited by 13 publications

References 35 publications

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals

Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

Method of Peanut Pod Quality Detection Based on Improved ResNet

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