Home Life Sciences Identification of winter wheat pests and diseases based on improved convolutional neural network
Article Open Access

Identification of winter wheat pests and diseases based on improved convolutional neural network

  • Jianbin Yao EMAIL logo , Jianhua Liu , Yingna Zhang and Hansheng Wang
Published/Copyright: July 6, 2023
Download Article (PDF)
Open Life Sciences
From the journal Open Life Sciences Volume 18 Issue 1

Abstract

Wheat pests and diseases are one of the main factors affecting wheat yield. According to the characteristics of four common pests and diseases, an identification method based on improved convolution neural network is proposed. VGGNet16 is selected as the basic network model, but the problem of small dataset size is common in specific fields such as smart agriculture, which limits the research and application of artificial intelligence methods based on deep learning technology in the field. Data expansion and transfer learning technology are introduced to improve the training mode, and then attention mechanism is introduced for further improvement. The experimental results show that the transfer learning scheme of fine-tuning source model is better than that of freezing source model, and the VGGNet16 based on fine-tuning all layers has the best recognition effect, with an accuracy of 96.02%. The CBAM-VGGNet16 and NLCBAM-VGGNet16 models are designed and implemented. The experimental results show that the recognition accuracy of the test set of CBAM-VGGNet16 and NLCBAM-VGGNet16 is higher than that of VGGNet16. The recognition accuracy of CBAM-VGGNet16 and NLCBAM-VGGNet16 is 96.60 and 97.57%, respectively, achieving high precision recognition of common pests and diseases of winter wheat.

Keywords: wheat diseases and pests; data expansion; transfer learning; mixed attention mechanism

1 Introduction

Wheat is one of the main food crops in China, and pests and diseases are a major problem in wheat production that seriously affects the yield and quality of wheat [1,2]. According to statistics, the planting area of wheat in China was as high as 23.57 million hm2 in 2021, and the annual output was 13.695 million tons. It is estimated that the occurrence area of wheat pests and diseases will reach 54 million hm2 in 2022. The overall situation is biased, including 27.3 million hm2 diseases and 26.7 million hm2 pests.

Traditional wheat pest identification mainly relies on staff patrols or the machine vision technology for auxiliary identification. Machine vision technology is an efficient method for automatic detection of crop pests and diseases, and its core is image processing. Currently, there is no general separation theory in image processing, resulting in insufficient scalability of the algorithm. In addition, the use of machine vision technology to identify winter wheat pests and diseases still has problems, such as complex treatment process, high labor cost, strong subjectivity, and difficulty in timely detection of large-scale infection of pests and diseases. In recent years, with the continuous development of artificial intelligence technology, deep learning [3] has gradually replaced machine learning and become the main representative of artificial intelligence technology, and has gradually been applied to the identification of crop pests and diseases.

As a representative model of deep learning, convolutional neural network (CNN) can automatically extract the features of diseases and pests, simplify the identification process of winter wheat pests and diseases, reduce labor costs, greatly improve the accuracy and stability of pests and diseases identification, and improve the identification efficiency. It can effectively promote the informatization and intelligence of agriculture, which is of great significance for the stable development of agriculture. For the identification of tomato pests and diseases, Fuentes et al. [4] added various types of refined filter groups on the traditional CNN to solve the problem of false alarm of the boundary box generator during the training process, as well as the problem of small sample number and unbalanced distribution of species, improving the identification accuracy to 96%. Yadav et al. [5] proposed a CNN model based on imaging methods, which realized the detection of bacterial spot disease of peach leaf, with a recognition accuracy of 98.75%. For small samples of hop pests and diseases, Lu and Chen improved the deep residual network (ResNet) model based on the attention mechanism, and the recognition accuracy of the improved model reached 93.27% [6]. Xiang et al. [7] proposed a plant pests and diseases identification method based on Xception model, which combined the multi-scale convolution and group convolution and introduced the dense connection mode to improve feature reuse between feature maps. The comprehensive accuracy of this method was 91.90%.

According to the research status of deep learning in the identification of pests and diseases, most of them mainly focus on large-leaf plants (tomatoes, cotton, grapes, and more), while that for small-leaf plants is relatively rare. To this end, taking the winter wheat as an object, a standardized dataset for the four common pest and disease samples collected in the natural environment of the test field is built. A traditional CNN model for the identification of winter wheat pests and diseases is constructed, and the migration learning technology and attention mechanism are introduced for improvement. Finally, the accurate identification of winter wheat common pests and diseases in the natural environment is realized.

2 Data and methods

2.1 Datasets

The dataset comes from the agricultural and water teaching practice base of North China University of Water Resources and Electric power, and is collected in the natural environment. The winter wheat variety is Xinhuamai 818, a new variety developed by Henan Agricultural University in 2018. The standardization of datasets includes sample sorting, preprocessing, and data enhancement.

  1. Dataset sorting and division. The dataset contains five categories: healthy wheat, aphid wheat, powdery mildew wheat, leaf rust wheat, and stripe rust wheat. The dataset contains 1,003 samples from different periods of wheat growth. Some samples are shown in Figure 1.

    According to the most common sample division ratio (8:2), each type of dataset is randomly divided into training sets and test sets. The composition of the datasets is shown in Table 1.

  2. Preprocessing. To meet the input sample size requirements of the CNN model, the nearest neighbor image interpolation method is used to uniformly process the original image to two specifications of 224 × 224 (pixels) and 227 × 227 (pixels) images. Normalization processing refers to obtaining the mean values of all samples in the dataset on the red, green, and blue channels separately, and then subtracting the mean values of each channel from each sample to obtain the standardized image. Normalization of images can effectively avoid gradient explosion during training.

  3. Data enhancement. The pre-processed image is enhanced to reduce the light interference. The image enhancement method based on scientific experiments and analysis, Retinex series algorithm, is selected to enhance the image. Figure 2 shows the enhancement of single scale retinex (SSR) [8], multi-scale retinex (MSR) [9], multi-scale retinex with color restoration (MSRCR) [10], and auto multi-scale retinex with color restoration (AutoMSRCR) [11] algorithms.

Table 1

Composition of winter wheat dataset

Categories Sample number/piece
Training sets Test sets Total
Aphids 36 10 46
Powdery mildew 58 15 73
Healthy winter wheat 113 30 143
Leaf rust 289 74 363
Stripe rust 301 77 378
Total 797 206 1,003
Figure 1 Wheat samples: (a) healthy wheat; (b) aphids; (c) powdery mildew; (d) leaf rust; and (e) stripe rust.
Figure 1

Wheat samples: (a) healthy wheat; (b) aphids; (c) powdery mildew; (d) leaf rust; and (e) stripe rust.

Figure 2 Image processing effects of different Retinex series algorithms: (a) original picture, (b) SSR, (c) MSR, (d) MSRCR, and (e) AutoMSRCR.
Figure 2

Image processing effects of different Retinex series algorithms: (a) original picture, (b) SSR, (c) MSR, (d) MSRCR, and (e) AutoMSRCR.

It can be seen from Figure 2 that the images enhanced by SSR and MSR algorithms are too dark, and a lot of image details will be lost. The image processed using the default parameters of the MSRCR algorithm are also not optimal. The AutoMSRCR algorithm is thus selected to enhance the samples.

2.2 CNNs

Considering the environmental conditions of hardware equipment, the following three convolutional neural network models with low requirements for computer hardware and excellent image classification effect are selected to realize the classification and recognition training of common pest and disease images of winter wheat.

  1. AlexNet network model, which won the championship in the 2012 image recognition contest with 57.1% accuracy and 80.2% top-5 recognition rate, has established the core position of CNN in image classification algorithm [12]. The model consists of five convolution layers and three full connection layers. ReLu activation function layer is set after the convolution layer and full connection layer, and LRN is set after the first two full connection layers, which is conducive to rapid convergence and generalization enhancement of the model. Zhou et al. [13] proposed a new method that uses AlexNet with ImageNet transfer learning as the feature extractor and optimized and regularized extreme learning as the classifier. The test outcome illustrates that on some apparel classification with style datasets, the precision, recall, F1-score, and accuracy of the proposed algorithm are 93.06, 93.17, 92.82, and 93.14%, respectively. The results verify that the raised algorithm significantly ameliorates the classification property of clothing image algorithms.

  2. VGGNet [14] network model is a series of neural network models proposed by the VGG experimental group of Oxford University, and has many variants. The most popular one is the VGGNet 16 network model, whose accuracy rate 92.3% on ImageNet ranks the top list 5. The VGGNet16 network model uses a smaller convolution kernel for feature extraction, including 13 volumelayers and 3 full connection layers. Also, these models, especially VGGNet 19 [15].

  3. Inception-V3 [16] network model. Such a series of networkmodels were proposed by the Google team in 2014. Before that, the CNN models were all based on stacking convolution layers to extract features. The Inception networkmodel indicativelyput forward the Inception module. According to various structures, the Inceis divided into multiple versions, hereof the Inception-V3 model is utilized to the most. Also, other CNNs, particularly ResNet18 [17], as well ResNet50 [18] improving.

2.3 Data expansion technology

Due to the problem of small dataset size and uneven distribution of sample sizes among different types in the manually collected basic dataset in this article, this section introduces data augmentation technology to expand the basic dataset, mainly using the following methods.

  1. Add random noise and random filtering. The dataset in this article was collected in the natural environment of the experimental field, so the background is relatively complex and there is also a lot of noise and light wave interference. Adding random noise and random filtering can reduce the interference of complex backgrounds on training results and better improve the generalization ability of the model. In this work, several common filters are selected, and random numbers are generated to make random selection among median filter, Gaussian blur filter, and other types.

  2. Perform random rotation and random offset. Due to the lack of unified regulations on the target position during the sampling process, and the existence of multi-angle issues such as positive and negative, far and near, this article randomly rotates and offsets the samples to improve the model’s generalization ability to target position changes.

  3. Add random color jitter. The collection of samples in the afternoon, on both sunny and cloudy days, will have significant differences in color. Therefore, random color jitter will be added to improve the model’s generalization ability to lighting factors. This article expands the sample by generating random numbers to randomly select from several changes in image saturation, brightness, contrast, and sharpness.

2.4 Transfer learning technology

Due to the small size of the dataset, it cannot meet the training of large network models, and the transfer learning technology [19] is introduced to improve the recognition of the model. Transfer learning can be divided into freezing source model and fine-tuning source model.

  1. Freezing source model refers to freezing the parameters of other layers of the source model during training, and modifying and training only the parameters of the last fully connected layer according to the classification number of the new dataset [20]. At this time, the source model is equivalent to a feature extractor.

  2. Fine-tuning source model refers to fine-tuning the parameters of the source model during training. According to different fine-tuning layers, the fine-tuning source model can be divided into fine-tuning part layers and fine-tuning all layers [21].

2.5 Mixed attention mechanism

CBAM module is a mixed attention module proposed by Sanghyun Woo et al. in 2018. This module integrates the channel attention mechanism and spatial attention mechanism, and gives different weights to each channel and each position of the feature map, respectively. In the channel domain, it enhances relevant channels and suppresses irrelevant channels; in the spatial domain, it filters out useful features and suppresses useless features. Its structure is shown in Figure 3.

Figure 3 Structural diagram of CBAM module.
Figure 3

Structural diagram of CBAM module.

It can be seen from Figure 3 that the spatial attention module of CBAM uses convolution to fuse the pooled feature maps. Due to the limitations of convolution, this module can only achieve the correlation between local regions, which limits the correlation between different locations with a certain distance. This work replaces the spatial attention module of CBAM module with the non-local attention mechanism of non-local module, to propose the NLCBAM module. On the basis of CBAM, it breaks through the limitation that convolution can only consider local regions and increases the receptive field of the network. The structure is shown in Figure 4.

Figure 4 Structural diagram of NLCBAM module.
Figure 4

Structural diagram of NLCBAM module.

2.6 Model evaluation index

The accuracy, precision rate of each classification, recall rate, and F 1 are used to evaluate the model. The calculation method of each index is as follows:

  1. Accuracy ( A 1 ) represents the proportion of correctly classified samples in the total samples of the dataset, and is calculated as follows:

    (1) A 1 = ∑ i = 1 N ( TP + TN ) ALL ,

    where, N is the category of datasets, N = 5, ALL represents the total number of samples in the dataset, TP represents the number of positive samples classified as positive samples in this category, and TN represents the number of negative samples classified as negative samples.

  2. The precision rate ( P 1 ) represents the proportion of the actual positive samples among the samples predicted to be positive samples in each category, and is calculated as follows:

    (2) P 1 = TP TP + FP ,

    where FP represents the number of negative samples classified as positive samples in this category.

  3. The recall rate ( R 1 ) represents the proportion of positive samples predicted to be positive samples in each category, and is calculated as follows:

    (3) R 1 = TP TP + FN ,

    where FN represents the number of positive samples classified as negative samples in this category.

  4. F1 ( F 1 ) is the average of the precision rate and recall rate, which is a comprehensive index, and is calculated as follows:

(4) F 1 = 2 ⁎ Precision ⁎ Recall Precision + Recall .

2.7 Test environment

The test machine is MacBook Air, with a CPU 1.6 GHz dual-core Intel Core i5, a memory of 8 GB, and a hard disk capacity of 256 GB. The operating system is macOS Monterey; the deep learning framework is Tensor Flow-CPU; the programming language is Python with 3.7.9 version, and the editor is PyCharm; the image processing library is cv2 and PIL, and the environment management software is Anaconda3.

3 Results analysis

3.1 Basic network selection

Based on the normalized dataset of winter wheat pests and diseases, this section realizes the identification of pests and diseases using AlexNet, VGGNet16, and Inception-V3 traditional CNNs. The models with the highest accuracy in all iterations of each network are selected as the final model of each network. To improve the training speed of the model, the training sets are trained in batches. The corresponding hyper parameters of each network model are shown in Table 2, and the test set evaluation indicators of each model are shown in Table 3.

Table 2

Hyper parameters corresponding to each network model

Parameters AlexNet VGGNet16 Inception-V3
Iterations/time 50 50 500
Number of samples per batch/piece 32 32 100
Loss function Softmax cross entropy Softmax cross entropy Softmax cross entropy
Optimizer Adam optimizer Adam optimizer Gradient descent optimizer
Learning rate Attenuation learning rate Attenuation learning rate Constant learning rate
Table 3

Test set evaluation index of each network model

Categories AlexNet VGGNet16 Inception-V3
P 1 R 1 F 1 P 1 R 1 F 1 P 1 R 1 F 1
Aphids 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Powdery mildew 0.2936 0.2625 0.2772 0.3315 0.2042 0.2527 0.3045 0.2032 0.2437
Healthy wheat 0.4352 0.5533 0.4872 0.8324 0.6618 0.7362 0.6583 0.7364 0.6952
Leaf rust 0.6473 0.6021 0.6239 0.7784 0.7435 0.7605 0.5932 0.6471 0.6189
Stripe rust 0.6483 0.7227 0.6835 0.6463 0.8829 0.7463 0.6364 0.6139 0.6249
Average 0.4049 0.4281 0.4143 0.5177 0.4981 0.4992 0.4385 0.4401 0.4366
A 1 0.5827 0.6914 0.6037

The parameters of Adam optimizer in the Table are set as beta_1 = 0.9 and beta_2 = 0.99. The initial learning rate of the attenuation learning rate method is set to 0.01, and the attenuation is 1/2 every 10 iterations. The learning rate of the constant learning rate method is always 0.01.

From Table 3, we can get the following four results: (1) The accuracy of the test set of AlexNet network model is 58.27%, and its recognition effect on stripe rust is the best. When F1 is 0.6835, it has the worst recognition on aphids; when F1 is 0, the recognition accuracy of powdery mildew is also low; when F1is 0.2772, the recognition accuracy is poor. (2) The accuracy of the test set of VGGNet16 model is 69.14%, and the recognition effect on leaf rust is the best. When F1 is 0.7605, the recognition effect on aphids is as poor as that of AlexNet model, and that on powdery mildew is also low, with F1 being only 0.2527. The effect of VGGNet16 model is better than AlexNet model. (3) The accuracy of Inception-V3 model is 60.37%. Its recognition effect on healthy wheat is the best, and F1 is 0.6952. The recognition effect on aphids is as poor as that of AlexNet model, and that on powdery mildew is also low, with F1 being only 0.2437. (4) In the three network models, VGGNet16 has the highest accuracy and is selected as the basic network.

3.2 Improvement based on data expansion

From the experimental results in the previous section, it can be seen that the F1 of aphids and powdery mildew is relatively low. Combined with the number of samples in each category, it is found that the recognition effect is positively correlated with the number of samples, as shown in Figure 5.

Figure 5 Number and F1 value of samples in each category of VGGNet16 final model.
Figure 5

Number and F1 value of samples in each category of VGGNet16 final model.

It can be seen from Figure 5 that the low recognition rate of individual types is caused by the small amount of data of this type and the uneven distribution of the number of each type of dataset. To solve these problems, the training dataset is randomly expanded by adding random noise and random filtering, random rotation and random shift, and random color jitter.

To ensure that the proportion of each sample in each category is consistent, and that the number of samples in each category after expansion is relatively balanced, this study randomly expands all samples in each category by multiple. The composition of the expanded dataset is shown in Table 4.

Table 4

Composition of the expanded winter wheat pests and diseases dataset

Categories Training set Test set/piece
Original data number/piece Random expansion times/times Data number after expansion/piece
Healthy wheat 113 7 791 30
Stripe rust 301 3 903 77
Leaf rust 289 3 867 74
Powdery mildew 58 14 812 15
Aphids 36 22 792 10
Total 797 4,165 206

The model is trained based on the expanded training set and the hyper-parameters corresponding to the VGGNet16 model in Table 2. The model evaluation indicators are shown in Table 5.

Table 5

Evaluation index of VGGNet16 test set after expansion

Categories P 1 R 1 F 1
Aphids 0.3641 0.4436 0.3999
Powdery mildew 0.4428 0.2872 0.3484
Healthy wheat 0.7394 0.7422 0.7408
Leaf rust 0.6435 0.7048 0.6728
Stripe rust 0.7471 0.7374 0.7422
Average 0.5874 0.5830 0.5808
A 1 0.6837

Table 5 shows that the accuracy of VGGNet16 model based on the expanded dataset is 68.37%. Compared with the results of the original data in Table 3, the recognition effect of the model on aphids has been significantly improved, and F1 changes from 0.0 to 0.3999. F1 of powdery mildew changes from 0.2527 to 0.3484, which is increased by 0.0957. This shows that the expanded training set samples can well solve the problem of low F1 recognition of aphids and powdery mildew using this model.

3.3 Improvement based on transfer learning

To solve the problem that the dataset is too small to train large models, the public large crop pests and diseases dataset Plant-Village is selected for migration learning of VGGNet16. First, the VGGNet16 model is fully trained based on the Plant-Village dataset, and then the number of neurons in the last fully connected layer of the model is modified. The training is continued on the winter wheat pests and diseases dataset. A total of four experimental protocols are designed according to the transfer learning technology, as shown in Figure 6.

Figure 6 Experimental scheme based on transfer learning. (a) VGGNet16, (b) Experiment 1, (c) Experiment 2, (d) Experiment 3, and (e) Experiment 4.
Figure 6

Experimental scheme based on transfer learning. (a) VGGNet16, (b) Experiment 1, (c) Experiment 2, (d) Experiment 3, and (e) Experiment 4.

In Figure 6, Experiment 1 adopts the transfer learning method of freezing source model, and the parameters of convolution layer (Convs), the first full connection layer (FC_1), and the second full connection layer (FC_2) are copied from the pre-training model. The number of neurons in the third full connection layer (FC_3) is modified to 5 and the parameters are randomly initialized for training. Experiment 2 adopts the transfer learning method of fine-tuning some layers. The parameters of Convs and FC_1 are copied from the pre-training model. After copying the parameters of FC_2, they are fine-tuned in training. The number of neurons in FC_3 is modified to 5 and the parameters are randomly initialized for training. Experiment 3 uses the transfer learning method of fine-tuning part of the layer, and the parameters of the Convs are copied from the pre-training model. After copying the parameters of FC_1 and FC_2, they are fine-tuned in training. The number of neurons in FC_3 is modified to 5 and the parameters are randomly initialized for training. Experiment 4 adopts the transfer learning method of fine-tuning all layers, and the parameters of the Convs are copied from the training model. After the parameters of FC_1 and FC_2 are copied from the pre-training model, they are fine-tuned in training. The number of neurons in FC_3 is modified to 5 and the parameters are randomly initialized for training.

Four transfer learning models are trained based on the expanded training set. To maintain consistency, the hyper-parameter setting of the transfer learning experiment is consistent with VGGNet16. The evaluation indicators of the test set corresponding to the four groups of transfer learning experiments are shown in Table 6.

Table 6

VGGNet16 test set evaluation index corresponding to each experimental scheme

Categories Experiment 1 Experiment 2 Experiment 3 Experiment 4
P 1 R 1 F 1 P 1 R 1 F 1 P 1 R 1 F 1 P 1 R 1 F 1
Aphids 0.2718 0.4375 0.3353 0.6225 0.5648 0.5922 0.6667 0.8889 0.7619 0.8182 1.0000 0.9000
Powdery mildew 0.4032 0.2924 0.3390 0.3345 0.2689 0.2981 0.7895 1.0000 0.8824 0.9333 0.9333 0.9333
Healthy 0.6185 0.6917 0.6531 0.8203 0.6192 0.7057 0.8182 0.9310 0.8710 0.9032 0.9655 0.9333
Leaf rust 0.7440 0.6642 0.7018 0.6733 0.7780 0.7219 0.9254 0.8611 0.8921 0.9855 0.9444 0.9645
Stripe rust 0.7093 0.7238 0.7165 0.7519 0.7427 0.7473 0.9429 0.8684 0.9041 0.9867 0.9737 0.9801
Average 0.5494 0.5619 0.5491 0.6405 0.5947 0.6130 0.8285 0.9099 0.8623 0.9254 0.9634 0.9423
A 1 0.6526 0.6937 0.8856 0.9602

As shown in Table 6, the accuracy of the freezing source model in Experiment 1 is only 65.26%, indicating that the transfer learning method of the freezing source model is not applicable to the dataset in this study. The accuracy of the two fine-tuning partial layers in Experiments 2 and 3 as well as the fine-tuning full layer in Experiment 4 are 69.37, 88.56, and 96.02%, respectively. It can be seen that when more layers are involved in fine-tuning, the better the recognition effect of the model will be. The transfer learning method based on fine-tuning all layers has the best effect.

3.4 Improvement based on attention mechanism

Since the dataset is sampled in the nutritional growth and reproductive growth stage of wheat, the color and characteristics of wheat pest and disease samples in this stage are quite different and the background of pest and disease samples in natural environment is complex. To this end, the CBAM-VGGNet16 and NLCBAM-VGGNet16 models are further designed. The addition of attention mechanism can enhance the role of relevant features during model training in the channel domain. At the same time, the interference of irrelevant location features on the classification model in the spatial domain can be suppressed. The VGGNet16 network diagram before and after improvement is shown in Figure 7.

Figure 7 Experimental scheme based on transfer learning. (a) Structural diagram of VGGNet16, (b) structural diagram of CBAM-VGGNet16, and (c) Structural diagram of NLCBAM-VGGNet16.
Figure 7

Experimental scheme based on transfer learning. (a) Structural diagram of VGGNet16, (b) structural diagram of CBAM-VGGNet16, and (c) Structural diagram of NLCBAM-VGGNet16.

In this section, the CBAM-VGGNet16 and NLCBAM-VGGNet16 network models are trained based on the expanded dataset and the experimental parameters are consistent with VGGNet16. The model was evaluated using the identification accuracy (Single A 1) of single pest and disease and the identification accuracy (A 1) of the test sets. In addition, the A-ResNet50 [6] and the Xception-CEMs network model [7] are selected to train based on the expanded dataset for comparison. The experimental results of the final model corresponding to each network model are shown in Table 7.

Table 7

Comparison of final model test results

Models Single A 1 A 1
Aphids Powdery mildew Healthy wheat Leaf rust Stripe rust
A-ResNet50 0.5000 0.6000 0.8000 0.8784 0.9090 0.8398
Xception-CEMs 0.6000 0.6000 0.8333 0.9189 0.9090 0.8640
VGGNet16 0.9000 0.9333 0.9333 0.9595 0.9740 0.9602
CBAM-VGGNet16 0.8000 0.9333 0.9667 0.9730 0.9870 0.9660
NLCBAM-VGGNet16 0.9000 0.9333 0.9667 1.0000 0.9740 0.9757

As shown in Table 7, the recognition accuracies of A-ResNet50 and Xception-CEMs for the test set in the study is only 83.98 and 86.40%, respectively, and the recognition is poor. The recognition effect of VGGNet16 after fine-tuning all layers is better, with 96.02%. Compared with the basic network VGGNet16, CBAM-VGGNet16 has improved the recognition accuracy of all kinds of pests and diseases except powdery mildew. This indicates that the addition of mixed attention mechanism can improve the overall recognition effect of VGGNet16 model. At the same time, compared with VGGNet16, NLCBAM-VGGNet16 has improved the recognition accuracy of all kinds of pests and diseases, and the accuracy can reach 97.57%. Compared with CBAM-VGGNet16, the recognition accuracy of all categories except stripe rust has been improved, indicating that the proposed NLCBAM module can more effectively improve the improvement effect of attention module on the recognition accuracy of network model than CBAM module.

In summary, both these models are improvements based on the attention mechanism of VGGNet16, with varying improvement effects. Compared to CBAM-VGGNet16, NLCBAM-VGGNet16 has a better improvement effect. But the model is also more complex, requiring more time and computational resources during the training process. Therefore, the selection of models should be based on accuracy requirements. If CBAM-VGGNet16 can meet the accuracy requirements, then choose CBAM-VGGNet16. If there are higher requirements, then choose NLCBAM-VGGNet16.

4 Conclusion

Aiming at the common pests and diseases of wheat, the dataset of winter wheat pests and diseases is standardized. The VGGNet16 CNN model is trained based on data expansion and migration learning technology, and finally the high-precision identification of common wheat pests and diseases is realized.

  1. In the three network models trained based on the improved dataset, VGGNet16 has the highest accuracy and is selected as the basic network for further optimization.

  2. The training set is expanded by data expansion technology, so that the number of samples in each category is balanced, and the problem of low recognition accuracy of aphids and powdery mildew in the recognition of VGGNet16 model is solved.

  3. The transfer learning experimental scheme of freezing source model, fine-tuning part of layers and fine-tuning all layers is designed. Compared with its recognition accuracy and the F1 of each category, the fine-tuning source model is better than the freezing source model. When more layers are involved in fine-tuning, the better the recognition effect of the model will be. The transfer learning method of fine-tuning all layers has the best effect, and the recognition accuracy can reach 96.02%.

  4. The recognition effect of CBAM-VGGNet16 and NLCBAM-VGGNet16 based on attention improvement is better than that of VGGNet16. The accuracy rate of the test set of CBAM-VGGNet16 is 96.60%, and that of NLCBAM-VGGNet16 is 97.57%, which realizes the high-precision identification of common pests and diseases of winter wheat.

Acknowledgments

The work was financed by the Key scientific and technological projects of Henan Province (222102110304), and National Natural Science Fund for Youth (51809095).The authors thank for the special funding and equipment support of the visiting professor at the school of materials science and engineering (Grant No.:4001-40734).

  1. Funding information: The work was supported by the Key scientific and technological projects of Henan Province (222102110304), and National Natural Science Fund for Youth (51809095). The authors thank for the special funding and equipment support of the visiting professor at the school of materials science and engineering (Grant No: 4001-40734).

  2. Conflict of interest: Authors state no conflict of interest.

  3. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

[1] National Bureau of Statistics. China Statistical Yearbook 2021. Beijing: China Statistics Press; 2022.Search in Google Scholar

[2] Nong X. Play a good “first hand game” in grain production to seize grain and promote harvest. J N Rural Technol. 2022;4:4–7. 10.3969/j.issn.1002-3542.2022.04.002. Chinese.Search in Google Scholar

[3] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. 10.1038/nature14539.Search in Google Scholar PubMed

[4] Fuentes A, Yoon S, Lee J, Park DS. High-performance deep neural network based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci. 2018;9:1162–77. 10.3389/fpls.2018.01162.Search in Google Scholar PubMed PubMed Central

[5] Yadav S, Sengar N, Singh A, Singh A, Dutta MK. Identification of disease using deep learning and evaluation of bacteriosis in peach leaf. Ecol Inf. 2021;61(1):101247–59. 10.1016/j.ecoinf.2021.101247.Search in Google Scholar

[6] Lu YN, Chen BC. Identification of hops pests and diseases in small samples based on attentional mechanisms. J Chin Agric Mechanization. 2021;42(3):189–96. 10.13733/j.jcam.issn.2095-5553.2021.03.026. Chinese.Search in Google Scholar

[7] Xiang XD, Zhai W, Huang YT, Liu W. Plant disease recognition based on Xception-CEMs neural network. J Chin Agric Mechanization. 2021;42(8):177–86. 10.13733/j.jcam.issn.2095-5553.2021.08.24. Chinese.Search in Google Scholar

[8] Aguirre-Castro OA, García-Guerrero EE, López-Bonilla OR, Tlelo-Cuautle E, López-Mancilla D, Cárdenas-Valdez JR, et al. Evaluation of underwater image enhancement algorithms based on Retinex and its implementation on embedded systems. Neurocomputing. 2022;494:148–59. 10.1016/j.neucom.2022.04.074.Search in Google Scholar

[9] Jin YY, Han XW, Zhou SN, Zhang SC. Overview of image enhancement algorithms. Comput Syst Application. 2021;30(6):18–27. 10.15888/j.cnki.csa.007956. Chinese.Search in Google Scholar

[10] Zhang S, Zhang Q, Li K. Detection of peanut kernel quality based on machine vision and adaptive convolution neural network. Trans CSAE. 2020;36:269–77. 10.11975/j.issn.1002-6819.2020.04.032. Chinese.Search in Google Scholar

[11] Guo X, Zhang Z, Han J, Li J, Hu X, Deng H. Research on water hazards detection method based on A-MSRCR and improved YOLO. In 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO); 2022. p. 1174–9. 10.1109/ROBIO55434.2022.10011695.Search in Google Scholar

[12] Krizhevsky A, Sutskever I, Hinton GE. Image net classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90. 10.1145/3065386.Search in Google Scholar

[13] Zhou ZY, Liu MX, Deng WX, Wang YM, Zhu ZF. Clothing image classification algorithm based on convolutional neural network and optimized regularized extreme learning machine. Text Res J. 2022;92(23–24):5106–24. 10.1177/00405175221115472 Chinese.Search in Google Scholar

[14] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ICLR.3rd International Conference on Learning Representations. ICLR: La Jolla; 2015. p. 1–14. 10.48550/arXiv.1409.1556.Search in Google Scholar

[15] Yang JY, Cai MD, Zhou ZY. Evolving convolution neural network by optimal regularization random vector functional link for computational color constancy. Opt Eng. 2022;61(10):103102. 10.1117/1.OE.61.10.103102.Search in Google Scholar

[16] Nikhitha M, Sri SR, Maheswari BU. Fruit recognition and grade of disease detection using inception v3 model. In. 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA); 2019. p. 1040–3. 10.1109/ICECA.2019.8822095.Search in Google Scholar

[17] Zhou ZY, Ma ZJ, Wang YM, Zhu ZF. Fabric wrinkle rating model based on ResNet18 and optimized random vector functional link network. Text Res J. 2023;93(1-2):172–93. 10.1177/00405175221117614.Search in Google Scholar

[18] Zhou ZY, Yang XF, Ji HD, Zhu ZF. Improving the classification accuracy of fishes and invertebrates using residual convolutional neural networks. ICES J Mar Sci. 2023;1–11. 10.1093/icesjms/fsad041.Search in Google Scholar

[19] Yao JB, Zhang YN, Liu JH. Identification of wheat diseases and insect pests based on convolutional neural network and transfer learning. J North China Univ Water Resour Electr power (Nat Sci). 2022;43(2):102–8. 10.19760/j.ncwu.zk.2022028 Chinese.Search in Google Scholar

[20] Long MS, Ouyang CJ, Liu H, Fu Q. Image recognition of Camellia oleifera diseases based on convolutional neural network & transfer learning. Trans Chin Soc Agric Eng. 2018;34(18):194–201. 10.11975/j.issn.1002-6819.2018.18.024. Chinese.Search in Google Scholar

[21] Xu JH, Shao MY, Wang YC, Han WT. Recognition of corn leaf spot and rust based on transfer learning with convolutional neural network. Trans Chin Soc Agric Machinery. 2020;51(2):230–6. 10.6041/j.issn.1000-1298.2020.02.025. Chinese.Search in Google Scholar
Received: 2023-02-23
Revised: 2023-04-07
Accepted: 2023-05-17
Published Online: 2023-07-06

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Biomedical Sciences
  2. Systemic investigation of inetetamab in combination with small molecules to treat HER2-overexpressing breast and gastric cancers
  3. Immunosuppressive treatment for idiopathic membranous nephropathy: An updated network meta-analysis
  4. Identifying two pathogenic variants in a patient with pigmented paravenous retinochoroidal atrophy
  5. Effects of phytoestrogens combined with cold stress on sperm parameters and testicular proteomics in rats
  6. A case of pulmonary embolism with bad warfarin anticoagulant effects caused by E. coli infection
  7. Neutrophilia with subclinical Cushing’s disease: A case report and literature review
  8. Isoimperatorin alleviates lipopolysaccharide-induced periodontitis by downregulating ERK1/2 and NF-κB pathways
  9. Immunoregulation of synovial macrophages for the treatment of osteoarthritis
  10. Novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant in a child patient with Joubert syndrome
  11. Antiphospholipid antibodies and the risk of thrombosis in myeloproliferative neoplasms
  12. Immunological responses of septic rats to combination therapy with thymosin α1 and vitamin C
  13. High glucose and high lipid induced mitochondrial dysfunction in JEG-3 cells through oxidative stress
  14. Pharmacological inhibition of the ubiquitin-specific protease 8 effectively suppresses glioblastoma cell growth
  15. Levocarnitine regulates the growth of angiotensin II-induced myocardial fibrosis cells via TIMP-1
  16. Age-related changes in peripheral T-cell subpopulations in elderly individuals: An observational study
  17. Single-cell transcription analysis reveals the tumor origin and heterogeneity of human bilateral renal clear cell carcinoma
  18. Identification of iron metabolism-related genes as diagnostic signatures in sepsis by blood transcriptomic analysis
  19. Long noncoding RNA ACART knockdown decreases 3T3-L1 preadipocyte proliferation and differentiation
  20. Surgery, adjuvant immunotherapy plus chemotherapy and radiotherapy for primary malignant melanoma of the parotid gland (PGMM): A case report
  21. Dosimetry comparison with helical tomotherapy, volumetric modulated arc therapy, and intensity-modulated radiotherapy for grade II gliomas: A single‑institution case series
  22. Soy isoflavone reduces LPS-induced acute lung injury via increasing aquaporin 1 and aquaporin 5 in rats
  23. Refractory hypokalemia with sexual dysplasia and infertility caused by 17α-hydroxylase deficiency and triple X syndrome: A case report
  24. Meta-analysis of cancer risk among end stage renal disease undergoing maintenance dialysis
  25. 6-Phosphogluconate dehydrogenase inhibition arrests growth and induces apoptosis in gastric cancer via AMPK activation and oxidative stress
  26. Experimental study on the optimization of ANM33 release in foam cells
  27. Primary retroperitoneal angiosarcoma: A case report
  28. Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia
  29. Malignant pleural effusion diagnosis and therapy
  30. Effect of spaceflight on the phenotype and proteome of Escherichia coli
  31. Comparison of immunotherapy combined with stereotactic radiotherapy and targeted therapy for patients with brain metastases: A systemic review and meta-analysis
  32. Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation
  33. Association between the VEGFR-2 -604T/C polymorphism (rs2071559) and type 2 diabetic retinopathy
  34. The role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis
  35. Triple-negative mouse breast cancer initiating cells show high expression of beta1 integrin and increased malignant features
  36. mNGS facilitates the accurate diagnosis and antibiotic treatment of suspicious critical CNS infection in real practice: A retrospective study
  37. The apatinib and pemetrexed combination has antitumor and antiangiogenic effects against NSCLC
  38. Radiotherapy for primary thyroid adenoid cystic carcinoma
  39. Design and functional preliminary investigation of recombinant antigen EgG1Y162–EgG1Y162 against Echinococcus granulosus
  40. Effects of losartan in patients with NAFLD: A meta-analysis of randomized controlled trial
  41. Bibliometric analysis of METTL3: Current perspectives, highlights, and trending topics
  42. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis
  43. PI3K/AKT/mTOR pathway and its related molecules participate in PROK1 silence-induced anti-tumor effects on pancreatic cancer
  44. The altered expression of cytoskeletal and synaptic remodeling proteins during epilepsy
  45. Effects of pegylated recombinant human granulocyte colony-stimulating factor on lymphocytes and white blood cells of patients with malignant tumor
  46. Prostatitis as initial manifestation of Chlamydia psittaci pneumonia diagnosed by metagenome next-generation sequencing: A case report
  47. NUDT21 relieves sevoflurane-induced neurological damage in rats by down-regulating LIMK2
  48. Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
  49. Exosomal HBV-DNA for diagnosis and treatment monitoring of chronic hepatitis B
  50. Shear stress leads to the dysfunction of endothelial cells through the Cav-1-mediated KLF2/eNOS/ERK signaling pathway under physiological conditions
  51. Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection
  52. Meta-analysis of the rs231775 locus polymorphism in the CTLA-4 gene and the susceptibility to Graves’ disease in children
  53. Cloning, subcellular localization and expression of phosphate transporter gene HvPT6 of hulless barley
  54. Coptisine mitigates diabetic nephropathy via repressing the NRLP3 inflammasome
  55. Significant elevated CXCL14 and decreased IL-39 levels in patients with tuberculosis
  56. Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation
  57. Gemella morbillorum infective endocarditis: A case report and literature review
  58. An unusual ectopic thymoma clonal evolution analysis: A case report
  59. Severe cumulative skin toxicity during toripalimab combined with vemurafenib following toripalimab alone
  60. Detection of V. vulnificus septic shock with ARDS using mNGS
  61. Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing
  62. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology
  63. Novel compound heterozygous mutations in TELO2 in an infant with You-Hoover-Fong syndrome: A case report and literature review
  64. ctDNA as a prognostic biomarker in resectable CLM: Systematic review and meta-analysis
  65. Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report
  66. Phylogenetic analysis of promoter regions of human Dolichol kinase (DOLK) and orthologous genes using bioinformatics tools
  67. Collagen changes in rabbit conjunctiva after conjunctival crosslinking
  68. Effects of NM23 transfection of human gastric carcinoma cells in mice
  69. Oral nifedipine and phytosterol, intravenous nicardipine, and oral nifedipine only: Three-arm, retrospective, cohort study for management of severe preeclampsia
  70. Case report of hepatic retiform hemangioendothelioma: A rare tumor treated with ultrasound-guided microwave ablation
  71. Curcumin induces apoptosis in human hepatocellular carcinoma cells by decreasing the expression of STAT3/VEGF/HIF-1α signaling
  72. Rare presentation of double-clonal Waldenström macroglobulinemia with pulmonary embolism: A case report
  73. Giant duplication of the transverse colon in an adult: A case report and literature review
  74. Ectopic thyroid tissue in the breast: A case report
  75. SDR16C5 promotes proliferation and migration and inhibits apoptosis in pancreatic cancer
  76. Vaginal metastasis from breast cancer: A case report
  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
https://doi.org/10.1515/biol-2022-0632
Keywords for this article
wheat diseases and pests; data expansion; transfer learning; mixed attention mechanism
Creative Commons
BY 4.0

Articles in the same Issue

  1. Biomedical Sciences
  2. Systemic investigation of inetetamab in combination with small molecules to treat HER2-overexpressing breast and gastric cancers
  3. Immunosuppressive treatment for idiopathic membranous nephropathy: An updated network meta-analysis
  4. Identifying two pathogenic variants in a patient with pigmented paravenous retinochoroidal atrophy
  5. Effects of phytoestrogens combined with cold stress on sperm parameters and testicular proteomics in rats
  6. A case of pulmonary embolism with bad warfarin anticoagulant effects caused by E. coli infection
  7. Neutrophilia with subclinical Cushing’s disease: A case report and literature review
  8. Isoimperatorin alleviates lipopolysaccharide-induced periodontitis by downregulating ERK1/2 and NF-κB pathways
  9. Immunoregulation of synovial macrophages for the treatment of osteoarthritis
  10. Novel CPLANE1 c.8948dupT (p.P2984Tfs*7) variant in a child patient with Joubert syndrome
  11. Antiphospholipid antibodies and the risk of thrombosis in myeloproliferative neoplasms
  12. Immunological responses of septic rats to combination therapy with thymosin α1 and vitamin C
  13. High glucose and high lipid induced mitochondrial dysfunction in JEG-3 cells through oxidative stress
  14. Pharmacological inhibition of the ubiquitin-specific protease 8 effectively suppresses glioblastoma cell growth
  15. Levocarnitine regulates the growth of angiotensin II-induced myocardial fibrosis cells via TIMP-1
  16. Age-related changes in peripheral T-cell subpopulations in elderly individuals: An observational study
  17. Single-cell transcription analysis reveals the tumor origin and heterogeneity of human bilateral renal clear cell carcinoma
  18. Identification of iron metabolism-related genes as diagnostic signatures in sepsis by blood transcriptomic analysis
  19. Long noncoding RNA ACART knockdown decreases 3T3-L1 preadipocyte proliferation and differentiation
  20. Surgery, adjuvant immunotherapy plus chemotherapy and radiotherapy for primary malignant melanoma of the parotid gland (PGMM): A case report
  21. Dosimetry comparison with helical tomotherapy, volumetric modulated arc therapy, and intensity-modulated radiotherapy for grade II gliomas: A single‑institution case series
  22. Soy isoflavone reduces LPS-induced acute lung injury via increasing aquaporin 1 and aquaporin 5 in rats
  23. Refractory hypokalemia with sexual dysplasia and infertility caused by 17α-hydroxylase deficiency and triple X syndrome: A case report
  24. Meta-analysis of cancer risk among end stage renal disease undergoing maintenance dialysis
  25. 6-Phosphogluconate dehydrogenase inhibition arrests growth and induces apoptosis in gastric cancer via AMPK activation and oxidative stress
  26. Experimental study on the optimization of ANM33 release in foam cells
  27. Primary retroperitoneal angiosarcoma: A case report
  28. Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia
  29. Malignant pleural effusion diagnosis and therapy
  30. Effect of spaceflight on the phenotype and proteome of Escherichia coli
  31. Comparison of immunotherapy combined with stereotactic radiotherapy and targeted therapy for patients with brain metastases: A systemic review and meta-analysis
  32. Activation of hypermethylated P2RY1 mitigates gastric cancer by promoting apoptosis and inhibiting proliferation
  33. Association between the VEGFR-2 -604T/C polymorphism (rs2071559) and type 2 diabetic retinopathy
  34. The role of IL-31 and IL-34 in the diagnosis and treatment of chronic periodontitis
  35. Triple-negative mouse breast cancer initiating cells show high expression of beta1 integrin and increased malignant features
  36. mNGS facilitates the accurate diagnosis and antibiotic treatment of suspicious critical CNS infection in real practice: A retrospective study
  37. The apatinib and pemetrexed combination has antitumor and antiangiogenic effects against NSCLC
  38. Radiotherapy for primary thyroid adenoid cystic carcinoma
  39. Design and functional preliminary investigation of recombinant antigen EgG1Y162–EgG1Y162 against Echinococcus granulosus
  40. Effects of losartan in patients with NAFLD: A meta-analysis of randomized controlled trial
  41. Bibliometric analysis of METTL3: Current perspectives, highlights, and trending topics
  42. Performance comparison of three scaling algorithms in NMR-based metabolomics analysis
  43. PI3K/AKT/mTOR pathway and its related molecules participate in PROK1 silence-induced anti-tumor effects on pancreatic cancer
  44. The altered expression of cytoskeletal and synaptic remodeling proteins during epilepsy
  45. Effects of pegylated recombinant human granulocyte colony-stimulating factor on lymphocytes and white blood cells of patients with malignant tumor
  46. Prostatitis as initial manifestation of Chlamydia psittaci pneumonia diagnosed by metagenome next-generation sequencing: A case report
  47. NUDT21 relieves sevoflurane-induced neurological damage in rats by down-regulating LIMK2
  48. Association of interleukin-10 rs1800896, rs1800872, and interleukin-6 rs1800795 polymorphisms with squamous cell carcinoma risk: A meta-analysis
  49. Exosomal HBV-DNA for diagnosis and treatment monitoring of chronic hepatitis B
  50. Shear stress leads to the dysfunction of endothelial cells through the Cav-1-mediated KLF2/eNOS/ERK signaling pathway under physiological conditions
  51. Interaction between the PI3K/AKT pathway and mitochondrial autophagy in macrophages and the leukocyte count in rats with LPS-induced pulmonary infection
  52. Meta-analysis of the rs231775 locus polymorphism in the CTLA-4 gene and the susceptibility to Graves’ disease in children
  53. Cloning, subcellular localization and expression of phosphate transporter gene HvPT6 of hulless barley
  54. Coptisine mitigates diabetic nephropathy via repressing the NRLP3 inflammasome
  55. Significant elevated CXCL14 and decreased IL-39 levels in patients with tuberculosis
  56. Whole-exome sequencing applications in prenatal diagnosis of fetal bowel dilatation
  57. Gemella morbillorum infective endocarditis: A case report and literature review
  58. An unusual ectopic thymoma clonal evolution analysis: A case report
  59. Severe cumulative skin toxicity during toripalimab combined with vemurafenib following toripalimab alone
  60. Detection of V. vulnificus septic shock with ARDS using mNGS
  61. Novel rare genetic variants of familial and sporadic pulmonary atresia identified by whole-exome sequencing
  62. The influence and mechanistic action of sperm DNA fragmentation index on the outcomes of assisted reproduction technology
  63. Novel compound heterozygous mutations in TELO2 in an infant with You-Hoover-Fong syndrome: A case report and literature review
  64. ctDNA as a prognostic biomarker in resectable CLM: Systematic review and meta-analysis
  65. Diagnosis of primary amoebic meningoencephalitis by metagenomic next-generation sequencing: A case report
  66. Phylogenetic analysis of promoter regions of human Dolichol kinase (DOLK) and orthologous genes using bioinformatics tools
  67. Collagen changes in rabbit conjunctiva after conjunctival crosslinking
  68. Effects of NM23 transfection of human gastric carcinoma cells in mice
  69. Oral nifedipine and phytosterol, intravenous nicardipine, and oral nifedipine only: Three-arm, retrospective, cohort study for management of severe preeclampsia
  70. Case report of hepatic retiform hemangioendothelioma: A rare tumor treated with ultrasound-guided microwave ablation
  71. Curcumin induces apoptosis in human hepatocellular carcinoma cells by decreasing the expression of STAT3/VEGF/HIF-1α signaling
  72. Rare presentation of double-clonal Waldenström macroglobulinemia with pulmonary embolism: A case report
  73. Giant duplication of the transverse colon in an adult: A case report and literature review
  74. Ectopic thyroid tissue in the breast: A case report
  75. SDR16C5 promotes proliferation and migration and inhibits apoptosis in pancreatic cancer
  76. Vaginal metastasis from breast cancer: A case report
  77. Screening of the best time window for MSC transplantation to treat acute myocardial infarction with SDF-1α antibody-loaded targeted ultrasonic microbubbles: An in vivo study in miniswine
  78. Inhibition of TAZ impairs the migration ability of melanoma cells
  79. Molecular complexity analysis of the diagnosis of Gitelman syndrome in China
  80. Effects of maternal calcium and protein intake on the development and bone metabolism of offspring mice
  81. Identification of winter wheat pests and diseases based on improved convolutional neural network
  82. Ultra-multiplex PCR technique to guide treatment of Aspergillus-infected aortic valve prostheses
  83. Virtual high-throughput screening: Potential inhibitors targeting aminopeptidase N (CD13) and PIKfyve for SARS-CoV-2
  84. Immune checkpoint inhibitors in cancer patients with COVID-19
  85. Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses
  86. Integrated analysis of the microbiome and transcriptome in stomach adenocarcinoma
  87. Berberine suppressed sarcopenia insulin resistance through SIRT1-mediated mitophagy
  88. DUSP2 inhibits the progression of lupus nephritis in mice by regulating the STAT3 pathway
  89. Lung abscess by Fusobacterium nucleatum and Streptococcus spp. co-infection by mNGS: A case series
  90. Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis
  91. Granulomatous polyangiitis involving the fourth ventricle: Report of a rare case and a literature review
  92. Studying infant mortality: A demographic analysis based on data mining models
  93. Metaplastic breast carcinoma with osseous differentiation: A report of a rare case and literature review
  94. Protein Z modulates the metastasis of lung adenocarcinoma cells
  95. Inhibition of pyroptosis and apoptosis by capsaicin protects against LPS-induced acute kidney injury through TRPV1/UCP2 axis in vitro
  96. TAK-242, a toll-like receptor 4 antagonist, against brain injury by alleviates autophagy and inflammation in rats
  97. Primary mediastinum Ewing’s sarcoma with pleural effusion: A case report and literature review
  98. Association of ADRB2 gene polymorphisms and intestinal microbiota in Chinese Han adolescents
  99. Tanshinone IIA alleviates chondrocyte apoptosis and extracellular matrix degeneration by inhibiting ferroptosis
  100. Study on the cytokines related to SARS-Cov-2 in testicular cells and the interaction network between cells based on scRNA-seq data
  101. Effect of periostin on bone metabolic and autophagy factors during tooth eruption in mice
  102. HP1 induces ferroptosis of renal tubular epithelial cells through NRF2 pathway in diabetic nephropathy
  103. Intravaginal estrogen management in postmenopausal patients with vaginal squamous intraepithelial lesions along with CO2 laser ablation: A retrospective study
  104. Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients
  105. Effects of physical exercise on biomarkers of oxidative stress in healthy subjects: A meta-analysis of randomized controlled trials
  106. Identification of lysosome-related genes in connection with prognosis and immune cell infiltration for drug candidates in head and neck cancer
  107. Development of an instrument-free and low-cost ELISA dot-blot test to detect antibodies against SARS-CoV-2
  108. Research progress on gas signal molecular therapy for Parkinson’s disease
  109. Adiponectin inhibits TGF-β1-induced skin fibroblast proliferation and phenotype transformation via the p38 MAPK signaling pathway
  110. The G protein-coupled receptor-related gene signatures for predicting prognosis and immunotherapy response in bladder urothelial carcinoma
  111. α-Fetoprotein contributes to the malignant biological properties of AFP-producing gastric cancer
  112. CXCL12/CXCR4/CXCR7 axis in placenta tissues of patients with placenta previa
  113. Association between thyroid stimulating hormone levels and papillary thyroid cancer risk: A meta-analysis
  114. Significance of sTREM-1 and sST2 combined diagnosis for sepsis detection and prognosis prediction
  115. Diagnostic value of serum neuroactive substances in the acute exacerbation of chronic obstructive pulmonary disease complicated with depression
  116. Research progress of AMP-activated protein kinase and cardiac aging
  117. TRIM29 knockdown prevented the colon cancer progression through decreasing the ubiquitination levels of KRT5
  118. Cross-talk between gut microbiota and liver steatosis: Complications and therapeutic target
  119. Metastasis from small cell lung cancer to ovary: A case report
  120. The early diagnosis and pathogenic mechanisms of sepsis-related acute kidney injury
  121. The effect of NK cell therapy on sepsis secondary to lung cancer: A case report
  122. Erianin alleviates collagen-induced arthritis in mice by inhibiting Th17 cell differentiation
  123. Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features
  124. Signalling pathways in the osteogenic differentiation of periodontal ligament stem cells
  125. Crosstalk between lactic acid and immune regulation and its value in the diagnosis and treatment of liver failure
  126. Clinicopathological features and differential diagnosis of gastric pleomorphic giant cell carcinoma
  127. Traumatic brain injury and rTMS-ERPs: Case report and literature review
  128. Extracellular fibrin promotes non-small cell lung cancer progression through integrin β1/PTEN/AKT signaling
  129. Knockdown of DLK4 inhibits non-small cell lung cancer tumor growth by downregulating CKS2
  130. The co-expression pattern of VEGFR-2 with indicators related to proliferation, apoptosis, and differentiation of anagen hair follicles
  131. Inflammation-related signaling pathways in tendinopathy
  132. CD4+ T cell count in HIV/TB co-infection and co-occurrence with HL: Case report and literature review
  133. Clinical analysis of severe Chlamydia psittaci pneumonia: Case series study
  134. Bioinformatics analysis to identify potential biomarkers for the pulmonary artery hypertension associated with the basement membrane
  135. Influence of MTHFR polymorphism, alone or in combination with smoking and alcohol consumption, on cancer susceptibility
  136. Catharanthus roseus (L.) G. Don counteracts the ampicillin resistance in multiple antibiotic-resistant Staphylococcus aureus by downregulation of PBP2a synthesis
  137. Combination of a bronchogenic cyst in the thoracic spinal canal with chronic myelocytic leukemia
  138. Bacterial lipoprotein plays an important role in the macrophage autophagy and apoptosis induced by Salmonella typhimurium and Staphylococcus aureus
  139. TCL1A+ B cells predict prognosis in triple-negative breast cancer through integrative analysis of single-cell and bulk transcriptomic data
  140. Ezrin promotes esophageal squamous cell carcinoma progression via the Hippo signaling pathway
  141. Ferroptosis: A potential target of macrophages in plaque vulnerability
  142. Predicting pediatric Crohn's disease based on six mRNA-constructed risk signature using comprehensive bioinformatic approaches
  143. Applications of genetic code expansion and photosensitive UAAs in studying membrane proteins
  144. HK2 contributes to the proliferation, migration, and invasion of diffuse large B-cell lymphoma cells by enhancing the ERK1/2 signaling pathway
  145. IL-17 in osteoarthritis: A narrative review
  146. Circadian cycle and neuroinflammation
  147. Probiotic management and inflammatory factors as a novel treatment in cirrhosis: A systematic review and meta-analysis
  148. Hemorrhagic meningioma with pulmonary metastasis: Case report and literature review
  149. SPOP regulates the expression profiles and alternative splicing events in human hepatocytes
  150. Knockdown of SETD5 inhibited glycolysis and tumor growth in gastric cancer cells by down-regulating Akt signaling pathway
  151. PTX3 promotes IVIG resistance-induced endothelial injury in Kawasaki disease by regulating the NF-κB pathway
  152. Pancreatic ectopic thyroid tissue: A case report and analysis of literature
  153. The prognostic impact of body mass index on female breast cancer patients in underdeveloped regions of northern China differs by menopause status and tumor molecular subtype
  154. Report on a case of liver-originating malignant melanoma of unknown primary
  155. Case report: Herbal treatment of neutropenic enterocolitis after chemotherapy for breast cancer
  156. The fibroblast growth factor–Klotho axis at molecular level
  157. Characterization of amiodarone action on currents in hERG-T618 gain-of-function mutations
  158. A case report of diagnosis and dynamic monitoring of Listeria monocytogenes meningitis with NGS
  159. Effect of autologous platelet-rich plasma on new bone formation and viability of a Marburg bone graft
  160. Small breast epithelial mucin as a useful prognostic marker for breast cancer patients
  161. Continuous non-adherent culture promotes transdifferentiation of human adipose-derived stem cells into retinal lineage
  162. Nrf3 alleviates oxidative stress and promotes the survival of colon cancer cells by activating AKT/BCL-2 signal pathway
  163. Favorable response to surufatinib in a patient with necrolytic migratory erythema: A case report
  164. Case report of atypical undernutrition of hypoproteinemia type
  165. Down-regulation of COL1A1 inhibits tumor-associated fibroblast activation and mediates matrix remodeling in the tumor microenvironment of breast cancer
  166. Sarcoma protein kinase inhibition alleviates liver fibrosis by promoting hepatic stellate cells ferroptosis
  167. Research progress of serum eosinophil in chronic obstructive pulmonary disease and asthma
  168. Clinicopathological characteristics of co-existing or mixed colorectal cancer and neuroendocrine tumor: Report of five cases
  169. Role of menopausal hormone therapy in the prevention of postmenopausal osteoporosis
  170. Precisional detection of lymph node metastasis using tFCM in colorectal cancer
  171. Advances in diagnosis and treatment of perimenopausal syndrome
  172. A study of forensic genetics: ITO index distribution and kinship judgment between two individuals
  173. Acute lupus pneumonitis resembling miliary tuberculosis: A case-based review
  174. Plasma levels of CD36 and glutathione as biomarkers for ruptured intracranial aneurysm
  175. Fractalkine modulates pulmonary angiogenesis and tube formation by modulating CX3CR1 and growth factors in PVECs
  176. Novel risk prediction models for deep vein thrombosis after thoracotomy and thoracoscopic lung cancer resections, involving coagulation and immune function
  177. Exploring the diagnostic markers of essential tremor: A study based on machine learning algorithms
  178. Evaluation of effects of small-incision approach treatment on proximal tibia fracture by deep learning algorithm-based magnetic resonance imaging
  179. An online diagnosis method for cancer lesions based on intelligent imaging analysis
  180. Medical imaging in rheumatoid arthritis: A review on deep learning approach
  181. Predictive analytics in smart healthcare for child mortality prediction using a machine learning approach
  182. Utility of neutrophil–lymphocyte ratio and platelet–lymphocyte ratio in predicting acute-on-chronic liver failure survival
  183. A biomedical decision support system for meta-analysis of bilateral upper-limb training in stroke patients with hemiplegia
  184. TNF-α and IL-8 levels are positively correlated with hypobaric hypoxic pulmonary hypertension and pulmonary vascular remodeling in rats
  185. Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation
  186. Comparison of the prognostic value of four different critical illness scores in patients with sepsis-induced coagulopathy
  187. Application and teaching of computer molecular simulation embedded technology and artificial intelligence in drug research and development
  188. Hepatobiliary surgery based on intelligent image segmentation technology
  189. Value of brain injury-related indicators based on neural network in the diagnosis of neonatal hypoxic-ischemic encephalopathy
  190. Analysis of early diagnosis methods for asymmetric dementia in brain MR images based on genetic medical technology
  191. Early diagnosis for the onset of peri-implantitis based on artificial neural network
  192. Clinical significance of the detection of serum IgG4 and IgG4/IgG ratio in patients with thyroid-associated ophthalmopathy
  193. Forecast of pain degree of lumbar disc herniation based on back propagation neural network
  194. SPA-UNet: A liver tumor segmentation network based on fused multi-scale features
  195. Systematic evaluation of clinical efficacy of CYP1B1 gene polymorphism in EGFR mutant non-small cell lung cancer observed by medical image
  196. Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke
  197. A novel approach for minimising anti-aliasing effects in EEG data acquisition
  198. ErbB4 promotes M2 activation of macrophages in idiopathic pulmonary fibrosis
  199. Clinical role of CYP1B1 gene polymorphism in prediction of postoperative chemotherapy efficacy in NSCLC based on individualized health model
  200. Lung nodule segmentation via semi-residual multi-resolution neural networks
  201. Evaluation of brain nerve function in ICU patients with Delirium by deep learning algorithm-based resting state MRI
  202. A data mining technique for detecting malignant mesothelioma cancer using multiple regression analysis
  203. Markov model combined with MR diffusion tensor imaging for predicting the onset of Alzheimer’s disease
  204. Effectiveness of the treatment of depression associated with cancer and neuroimaging changes in depression-related brain regions in patients treated with the mediator-deuterium acupuncture method
  205. Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis
  206. Monitoring and evaluation of anesthesia depth status data based on neuroscience
  207. Exploring the conformational dynamics and thermodynamics of EGFR S768I and G719X + S768I mutations in non-small cell lung cancer: An in silico approaches
  208. Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer
  209. Incidence of different pressure patterns of spinal cerebellar ataxia and analysis of imaging and genetic diagnosis
  210. Pathogenic bacteria and treatment resistance in older cardiovascular disease patients with lung infection and risk prediction model
  211. Adoption value of support vector machine algorithm-based computed tomography imaging in the diagnosis of secondary pulmonary fungal infections in patients with malignant hematological disorders
  212. From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology
  213. Ecology and Environmental Science
  214. Monitoring of hourly carbon dioxide concentration under different land use types in arid ecosystem
  215. Comparing the differences of prokaryotic microbial community between pit walls and bottom from Chinese liquor revealed by 16S rRNA gene sequencing
  216. Effects of cadmium stress on fruits germination and growth of two herbage species
  217. Bamboo charcoal affects soil properties and bacterial community in tea plantations
  218. Optimization of biogas potential using kinetic models, response surface methodology, and instrumental evidence for biodegradation of tannery fleshings during anaerobic digestion
  219. Understory vegetation diversity patterns of Platycladus orientalis and Pinus elliottii communities in Central and Southern China
  220. Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve
  221. Food Science
  222. Effect of berrycactus fruit (Myrtillocactus geometrizans) on glutamate, glutamine, and GABA levels in the frontal cortex of rats fed with a high-fat diet
  223. Guesstimate of thymoquinone diversity in Nigella sativa L. genotypes and elite varieties collected from Indian states using HPTLC technique
  224. Analysis of bacterial community structure of Fuzhuan tea with different processing techniques
  225. Untargeted metabolomics reveals sour jujube kernel benefiting the nutritional value and flavor of Morchella esculenta
  226. Mycobiota in Slovak wine grapes: A case study from the small Carpathians wine region
  227. Elemental analysis of Fadogia ancylantha leaves used as a nutraceutical in Mashonaland West Province, Zimbabwe
  228. Microbiological transglutaminase: Biotechnological application in the food industry
  229. Influence of solvent-free extraction of fish oil from catfish (Clarias magur) heads using a Taguchi orthogonal array design: A qualitative and quantitative approach
  230. Chromatographic analysis of the chemical composition and anticancer activities of Curcuma longa extract cultivated in Palestine
  231. The potential for the use of leghemoglobin and plant ferritin as sources of iron
  232. Investigating the association between dietary patterns and glycemic control among children and adolescents with T1DM
  233. Bioengineering and Biotechnology
  234. Biocompatibility and osteointegration capability of β-TCP manufactured by stereolithography 3D printing: In vitro study
  235. Clinical characteristics and the prognosis of diabetic foot in Tibet: A single center, retrospective study
  236. Agriculture
  237. Biofertilizer and NPSB fertilizer application effects on nodulation and productivity of common bean (Phaseolus vulgaris L.) at Sodo Zuria, Southern Ethiopia
  238. On correlation between canopy vegetation and growth indexes of maize varieties with different nitrogen efficiencies
  239. Exopolysaccharides from Pseudomonas tolaasii inhibit the growth of Pleurotus ostreatus mycelia
  240. A transcriptomic evaluation of the mechanism of programmed cell death of the replaceable bud in Chinese chestnut
  241. Melatonin enhances salt tolerance in sorghum by modulating photosynthetic performance, osmoregulation, antioxidant defense, and ion homeostasis
  242. Effects of plant density on alfalfa (Medicago sativa L.) seed yield in western Heilongjiang areas
  243. Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique
  244. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture
  245. Animal Sciences
  246. Effect of ketogenic diet on exercise tolerance and transcriptome of gastrocnemius in mice
  247. Combined analysis of mRNA–miRNA from testis tissue in Tibetan sheep with different FecB genotypes
  248. Isolation, identification, and drug resistance of a partially isolated bacterium from the gill of Siniperca chuatsi
  249. Tracking behavioral changes of confined sows from the first mating to the third parity
  250. The sequencing of the key genes and end products in the TLR4 signaling pathway from the kidney of Rana dybowskii exposed to Aeromonas hydrophila
  251. Development of a new candidate vaccine against piglet diarrhea caused by Escherichia coli
  252. Plant Sciences
  253. Crown and diameter structure of pure Pinus massoniana Lamb. forest in Hunan province, China
  254. Genetic evaluation and germplasm identification analysis on ITS2, trnL-F, and psbA-trnH of alfalfa varieties germplasm resources
  255. Tissue culture and rapid propagation technology for Gentiana rhodantha
  256. Effects of cadmium on the synthesis of active ingredients in Salvia miltiorrhiza
  257. Cloning and expression analysis of VrNAC13 gene in mung bean
  258. Chlorate-induced molecular floral transition revealed by transcriptomes
  259. Effects of warming and drought on growth and development of soybean in Hailun region
  260. Effects of different light conditions on transient expression and biomass in Nicotiana benthamiana leaves
  261. Comparative analysis of the rhizosphere microbiome and medicinally active ingredients of Atractylodes lancea from different geographical origins
  262. Distinguish Dianthus species or varieties based on chloroplast genomes
  263. Comparative transcriptomes reveal molecular mechanisms of apple blossoms of different tolerance genotypes to chilling injury
  264. Study on fresh processing key technology and quality influence of Cut Ophiopogonis Radix based on multi-index evaluation
  265. An advanced approach for fig leaf disease detection and classification: Leveraging image processing and enhanced support vector machine methodology
  266. Erratum
  267. Erratum to “Protein Z modulates the metastasis of lung adenocarcinoma cells”
  268. Erratum to “BRCA1 subcellular localization regulated by PI3K signaling pathway in triple-negative breast cancer MDA-MB-231 cells and hormone-sensitive T47D cells”
  269. Retraction
  270. Retraction to “Protocatechuic acid attenuates cerebral aneurysm formation and progression by inhibiting TNF-alpha/Nrf-2/NF-kB-mediated inflammatory mechanisms in experimental rats”
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2026 De Gruyter Brill
Downloaded on 27.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/biol-2022-0632/html
Scroll to top button