DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

Liu, Yujian; Hu, Yaowen; Cai, Weiwei; Zhou, Guofu; Zhan, Jialei; Li, Liujun

doi:10.1155/2022/4848425

Cited by 12 publications

(10 citation statements)

References 33 publications

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“…It involves isolating leaves from plant images and has extensive research significance and real-world applications. With improving computer capabilities, the use of deep learning in agriculture is becoming a key trend [1,2]. Whether we realize it or not, AI has become an integral part of our everyday existence, assuming innovative roles in various sectors such as industry, healthcare, transportation, education, and numerous other domains that directly impact the lives of the public [3].…”

Section: Introductionmentioning

confidence: 99%

Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

Jiang,

Chen,

Wang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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Image segmentation holds significant research significance and practical applications in the fields of computer vision and image processing, particularly in agricultural production, environmental monitoring, and digital botany. However, there is relatively limited research on its application to paper mulberry, prompting us to construct our own semantic segmentation dataset for paper mulberry. This dataset comprises 2576 images, which were meticulously annotated using the eiseg tool. Data augmentation techniques were also employed during the training process. Regarding the model, this study is built upon the DeepLabV3+ architecture, with the introduction of an attention mechanism known as the Squeeze-and-Excitation Block (SE-Block). Furthermore, modifications to the dilation rates of atrous convolutions within the ASPP module were made to achieve a larger receptive field. Experimental results demonstrate a notable improvement in segmentation accuracy, with a gain of 0.8% over the original algorithm. This enhancement is particularly beneficial for precise leaf area calculations.

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Section: Introductionmentioning

confidence: 99%

Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

Jiang,

Chen,

Wang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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“…With the improvement of the computer performance, applying deep learning to agricultural production is the development trend of agriculture in the future[ 12 , 13 ]. Deep learning automatically extracts image features by introducing operations such as convolution layer, pooling layer, and full connection layer, which makes a breakthrough in plant leaf disease recognition.…”

Section: Introductionmentioning

confidence: 99%

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

Deng

Zhou

et al. 2023

Plant Phenomics

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Tomato disease control is an urgent requirement in the field of intellectual agriculture, and one of the keys to it is quantitative identification and precise segmentation of tomato leaf diseases. Some diseased areas on tomato leaves are tiny and may go unnoticed during segmentation. Blurred edge also makes the segmentation accuracy poor. Based on UNet, we propose an effective image-based tomato leaf disease segmentation method called Cross-layer Attention Fusion Mechanism combined with Multi-scale Convolution Module (MC-UNet). First, a Multi-scale Convolution Module is proposed. This module obtains multiscale information about tomato disease by employing 3 convolution kernels of different sizes, and it highlights the edge feature information of tomato disease using the Squeeze-and-Excitation Module. Second, a Cross-layer Attention Fusion Mechanism is proposed. This mechanism highlights tomato leaf disease locations via gating structure and fusion operation. Then, we employ SoftPool rather than MaxPool to retain valid information on tomato leaves. Finally, we use the SeLU function appropriately to avoid network neuron dropout. We compared MC-UNet to the existing segmentation network on our self-built tomato leaf disease segmentation dataset and MC-UNet achieved 91.32% accuracy and 6.67M parameters. Our method achieves good results for tomato leaf disease segmentation, which demonstrates the effectiveness of the proposed methods.

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“…However, such methods require specialized equipment and operational skills that make them universally di cult to adopt [4,5]. With the rapid development of arti cial intelligence to promote precision agriculture, arti cial intelligence (AI), machine learning (ML), and computer vision (CV) technologies are used for automatic crop leaf disease detection [6][7][8], which are time-sensitive and e cient and requires less human intervention, providing a reliable technical means for accurate detection of crop leaf diseases.…”

Section: Introductionmentioning

confidence: 99%

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

Dai

Fan

2022

Computational Intelligence and Neuroscience

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To solve the problems of weak generalization of potato early and late blight recognition models in real complex scenarios, susceptibility to interference from crop varieties, colour characteristics, leaf spot shapes, disease cycles and environmental factors, and strong dependence on storage and computational resources, an improved YOLO v5 model (DA-ActNN-YOLOV5) is proposed to study potato diseases of different cycles in multiple regional scenarios. Thirteen data augmentation techniques were used to expand the data to improve model generalization and prevent overfitting; potato leaves were extracted by YOLO v5 image segmentation and labelled with LabelMe for building data samples; the component modules of the YOLO v5 network were replaced using model compression technology (ActNN) for potato disease detection when the device is low on memory. Based on this, the features extracted from all network layers are visualized, and the extraction of features from each network layer can be distinguished, from which an understanding of the feature learning behavior of the deep model can be obtained. The results show that in the scenario of multiple complex factors interacting, the identification accuracy of early and late potato blight in this study reached 99.81%. The introduced data augmentation technique improved the average accuracy by 9.22%. Compared with the uncompressed YOLO v5 model, the integrated ActNN runs more efficiently, the accuracy loss due to compressed parameters is less than 0.65%, and the time consumption does not exceed 30 min, which saves a lot of computational cost and time. In summary, this research method can accurately identify potato early and late blight in various scenarios.

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DCCAM-MRNet: Mixed Residual Connection Network with Dilated Convolution and Coordinate Attention Mechanism for Tomato Disease Identification

Cited by 12 publications

References 33 publications

Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

An Effective Image-Based Tomato Leaf Disease Segmentation Method Using MC-UNet

DA-ActNN-YOLOV5: Hybrid YOLO v5 Model with Data Augmentation and Activation of Compression Mechanism for Potato Disease Identification

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