Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM

Ma, Rui; Wang, Jia; Zhao, Wei; Guo, Hongjie; Dai, Dongnan; Yun, Yuliang; Li, Li; Hao, Fengqi; Bai, Jinqiang; Ma, Dexin

doi:10.3390/agriculture13010011

Cited by 20 publications

(15 citation statements)

References 43 publications

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“…The attention mechanism has demonstrated strong performance in previous research on tasks like categorization, detection, and segmentation ( Karthik et al., 2020 ; Mi et al., 2020 ). In this study, we thoroughly examined SENet ( Hu et al., 2020 ), ECANet ( Yu et al., 2022 ), and CBAM ( Ma et al., 2022 ), three attention mechanisms, and we chose the best module to enhance apple leaf spot segmentation.…”

Section: Improved U-net Network Structurementioning

confidence: 99%

Research of segmentation recognition of small disease spots on apple leaves based on hybrid loss function and CBAM

Zhang

et al. 2023

Front. Plant Sci.

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Identification technology of apple diseases is of great significance in improving production efficiency and quality. This paper has used apple Alternaria blotch and brown spot disease leaves as the research object and proposes a disease spot segmentation and disease identification method based on DFL-UNet+CBAM to address the problems of low recognition accuracy and poor performance of small spot segmentation in apple leaf disease recognition. The goal of this paper is to accurately prevent and control apple diseases, avoid fruit quality degradation and yield reduction, and reduce the resulting economic losses. DFL-UNet+CBAM model has employed a hybrid loss function of Dice Loss and Focal Loss as the loss function and added CBAM attention mechanism to both effective feature layers extracted by the backbone network and the results of the first upsampling, enhancing the model to rescale the inter-feature weighting relationships, enhance the channel features of leaf disease spots and suppressing the channel features of healthy parts of the leaf, and improving the network’s ability to extract disease features while also increasing model robustness. In general, after training, the average loss rate of the improved model decreases from 0.063 to 0.008 under the premise of ensuring the accuracy of image segmentation. The smaller the loss value is, the better the model is. In the lesion segmentation and disease identification test, MIoU was 91.07%, MPA was 95.58%, F1 Score was 95.16%, MIoU index increased by 1.96%, predicted disease area and actual disease area overlap increased, MPA increased by 1.06%, predicted category correctness increased, F1 Score increased by 1.14%, the number of correctly identified lesion pixels increased, and the segmentation result was more accurate. Specifically, compared to the original U-Net model, the segmentation of Alternaria blotch disease, the MIoU value increased by 4.41%, the MPA value increased by 4.13%, the Precision increased by 1.49%, the Recall increased by 4.13%, and the F1 Score increased by 2.81%; in the segmentation of brown spots, MIoU values increased by 1.18%, MPA values by 0.6%, Precision by 0.78%, Recall by 0.6%, and F1 Score by 0.69%. The spot diameter of the Alternaria blotch disease is 0.2-0.3cm in the early stage, 0.5-0.6cm in the middle and late stages, and the spot diameter of the brown spot disease is 0.3-3cm. Obviously, brown spot spots are larger than Alternaria blotch spots. The segmentation performance of smaller disease spots has increased more noticeably, according to the quantitative analysis results, proving that the model’s capacity to segment smaller disease spots has greatly improved. The findings demonstrate that for the detection of apple leaf diseases, the method suggested in this research has a greater recognition accuracy and better segmentation performance. The model in this paper can obtain more sophisticated semantic information in comparison to the traditional U-Net, further enhance the recognition accuracy and segmentation performance of apple leaf spots, and address the issues of low accuracy and low efficiency of conventional disease recognition methods as well as the challenging convergence of conventional deep convolutional networks.

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Section: Improved U-net Network Structurementioning

confidence: 99%

Research of segmentation recognition of small disease spots on apple leaves based on hybrid loss function and CBAM

Zhang

et al. 2023

Front. Plant Sci.

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“…MobileNetV2 is an upgraded version of MobileNetV1 that maintains simplicity, eliminates the need for special operators, and improves accuracy [42]. The key enhancement in MobileNetV2 is the introduction of a new activation function called ReLU6 [43], which restricts the maximum output value to 6. The purpose behind this design is to ensure high numerical resolution even in scenarios with low precision.…”

Section: Backbone Feature Extraction Networkmentioning

confidence: 99%

“…The CBAM structure [43], illustrated in Figure 3, comprises the channel attention module and the spatial attention module. Within the channel attention module, pooling is applied to the input feature map to acquire weight information for each channel.…”

Section: Convolutional Block Attention Modulementioning

confidence: 99%

Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network

Li,

Zhang,

Wang

et al. 2023

Remote Sensing

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China is one of the countries with the largest citrus cultivation areas, and its citrus industry has received significant attention due to its substantial economic benefits. Traditional manual forestry surveys and remote sensing image classification tasks are labor-intensive and time-consuming, resulting in low efficiency. Remote sensing technology holds great potential for obtaining spatial information on citrus orchards on a large scale. This study proposes a lightweight model for citrus plantation extraction that combines the DeepLabV3+ model with the convolutional block attention module (CBAM) attention mechanism, with a focus on the phenological growth characteristics of citrus in the Guangxi region. The objective is to address issues such as inaccurate extraction of citrus edges in high-resolution images, misclassification and omissions caused by intra-class differences, as well as the large number of network parameters and long training time found in classical semantic segmentation models. To reduce parameter count and improve training speed, the MobileNetV2 lightweight network is used as a replacement for the Xception backbone network in DeepLabV3+. Additionally, the CBAM is introduced to extract citrus features more accurately and efficiently. Moreover, in consideration of the growth characteristics of citrus, this study augments the feature input with additional channels to better capture and utilize key phenological features of citrus, thereby enhancing the accuracy of citrus recognition. The results demonstrate that the improved DeepLabV3+ model exhibits high reliability in citrus recognition and extraction, achieving an overall accuracy (OA) of 96.23%, a mean pixel accuracy (mPA) of 83.79%, and a mean intersection over union (mIoU) of 85.40%. These metrics represent an improvement of 11.16%, 14.88%, and 14.98%, respectively, compared to the original DeepLabV3+ model. Furthermore, when compared to classical semantic segmentation models, such as UNet and PSPNet, the proposed model achieves higher recognition accuracy. Additionally, the improved DeepLabV3+ model demonstrates a significant reduction in both parameters and training time. Generalization experiments conducted in Nanning, Guangxi Province, further validate the model’s strong generalization capabilities. Overall, this study emphasizes extraction accuracy, reduction in parameter count, adherence to timeliness requirements, and facilitation of rapid and accurate extraction of citrus plantation areas, presenting promising application prospects.

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“…Ma et al. (2023) added CBAM into MobileNetV2 and improved CBAM by replacing cascade connection with parallel connection to obtain I_CBAM_MobileNetV2, which achieved 98.2% accuracy in detecting image features of maize seed varieties.…”

Section: Introductionmentioning

confidence: 99%

Rice leaf disease detection based on enhanced feature fusion and target adaptation

Li,

Yang,

et al. 2024

Plant Pathology

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Intelligent rice disease recognition methods based on deep neural networks can predict the degree of disease on the basis of, for example, the number of disease spots on an image, so that preventive measures can be taken. Currently, intelligent recognition methods for rice diseases suffer from the disadvantages of poor versatility and low accuracy. This paper uses eight common image classification networks to classify and identify four rice diseases. ResNet50 was selected as the feature extraction network and an enhanced feature fusion and target adaptive network (EFFTAN), referred to as EFFTAN, is proposed. The EFFTAN was used to detect four rice spot diseases in the rice leaf disease image samples dataset; the mean average precision of the final detection was 95.3%, and effective detection was also achieved for the dense spot features.

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Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM

Cited by 20 publications

References 43 publications

Research of segmentation recognition of small disease spots on apple leaves based on hybrid loss function and CBAM

Research of segmentation recognition of small disease spots on apple leaves based on hybrid loss function and CBAM

Extracting Citrus in Southern China (Guangxi Region) Based on the Improved DeepLabV3+ Network

Rice leaf disease detection based on enhanced feature fusion and target adaptation

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