Lightweight dense-scale network (LDSNet) for corn leaf disease identification

Zeng, Weihui; Li, Haidong; Hu, Gensheng; Liang, Dong

doi:10.1016/j.compag.2022.106943

Cited by 71 publications

(29 citation statements)

References 20 publications

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“…Agriculture 2022, 12, x FOR PEER REVIEW 3 of 18 lightweight dense-scale network (LDSNet) for corn leaf disease identification under field conditions [20]. The accuracy of the optimized model on the test data was 95.4%.…”

Section: Image Acquisitionmentioning

confidence: 99%

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GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

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Section: Image Acquisitionmentioning

confidence: 99%

“…The experimental results showed that the average accuracy of the model was 99.71% on the open-source dataset. Zeng et al proposed a lightweight dense-scale network (LDSNet) for corn leaf disease identification under field conditions [20]. The accuracy of the optimized model on the test data was 95.4%.…”

Section: Introductionmentioning

confidence: 99%

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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“…The improved network has higher disease recognition accuracy on the PantVillage dataset while reducing the model complexity. Zeng, W. et al [ 29 ] proposed a lightweight dense scale network model (LDSNet) that can be used to identify corn diseases in complex backgrounds, and the core module of the model was the improved dense dilated convolution (IDDC), and a new loss function was proposed to optimize the network model, the accuracy of the model reaches 95.4%, and the number of parameters only accounts for 45.4% of ShuffleNetV2. Although the above studies have achieved good results, there is still room for improvement in the models.…”

Section: Related Workmentioning

confidence: 99%

Development of a Lightweight Crop Disease Image Identification Model Based on Attentional Feature Fusion

Cheng

Liu

Qiu

et al. 2022

Sensors

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Crop diseases are one of the important factors affecting crop yield and quality and are also an important research target in the field of agriculture. In order to quickly and accurately identify crop diseases, help farmers to control crop diseases in time, and reduce crop losses. Inspired by the application of convolutional neural networks in image identification, we propose a lightweight crop disease image identification model based on attentional feature fusion named DSGIResNet_AFF, which introduces self-built lightweight residual blocks, inverted residuals blocks, and attentional feature fusion modules on the basis of ResNet18. We apply the model to the identification of rice and corn diseases, and the results show the effectiveness of the model on the real dataset. Additionally, the model is compared with other convolutional neural networks (AlexNet, VGG16, ShuffleNetV2, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large), and the experimental results show that the accuracy, sensitivity, F1-score, AUC of the proposed model DSGIResNet_AFF are 98.30%, 98.23%, 98.24%, 99.97%, respectively, which are better than other network models, while the complexity of the model is significantly reduced (compared with the basic model ResNet18, the number of parameters is reduced by 94.10%, and the floating point of operations(FLOPs) is reduced by 86.13%). The network model DSGIResNet_AFF can be applied to mobile devices and become a useful tool for identifying crop diseases.

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“…The attention mechanism is an effective supplementary method for improving traditional feature extraction. In ( Zeng et al., 2022a ), the authors proposed a lightweight dense-scale network (LDSNet) that combined dense dilated convolutional blocks and a coordinated attention fusion mechanism for the identification of maize diseases. The dilated convolutional layers improved the model receptive field and provided computation of disease features at different scales.…”

Section: Related Workmentioning

confidence: 99%

“…Particularly for crop leaf disease detection, identifying and focusing on diseaseaffected areas is critical for attaining high classification accuracy (Zeng and Li, 2020;Yang et al, 2020;Zhu et al, 2021). Limited studies have investigated attention techniques for the precise categorization of maize leaf disease (Chen et al, 2021;Zeng et al, 2022a;Qian et al, 2022). Despite tremendous improvements, there is still a need for improvement in diagnosing and classifying maize leaf disease in actual field situations.…”

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confidence: 99%

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

Albahli

Masood

2022

Front. Plant Sci.

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Maize leaf disease significantly reduces the quality and overall crop yield. Therefore, it is crucial to monitor and diagnose illnesses during the growth season to take necessary actions. However, accurate identification is challenging to achieve as the existing automated methods are computationally complex or perform well on images with a simple background. Whereas, the realistic field conditions include a lot of background noise that makes this task difficult. In this study, we presented an end-to-end learning CNN architecture, Efficient Attention Network (EANet) based on the EfficientNetv2 model to identify multi-class maize crop diseases. To further enhance the capacity of the feature representation, we introduced a spatial-channel attention mechanism to focus on affected locations and help the detection network accurately recognize multiple diseases. We trained the EANet model using focal loss to overcome class-imbalanced data issues and transfer learning to enhance network generalization. We evaluated the presented approach on the publically available datasets having samples captured under various challenging environmental conditions such as varying background, non-uniform light, and chrominance variances. Our approach showed an overall accuracy of 99.89% for the categorization of various maize crop diseases. The experimental and visual findings reveal that our model shows improved performance compared to conventional CNNs, and the attention mechanism properly accentuates the disease-relevant information by ignoring the background noise.

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Lightweight dense-scale network (LDSNet) for corn leaf disease identification

Cited by 71 publications

References 20 publications

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

Development of a Lightweight Crop Disease Image Identification Model Based on Attentional Feature Fusion

Efficient attention-based CNN network (EANet) for multi-class maize crop disease classification

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