Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

Liu, Mingxin; Liang, Hui; Hou, Mingxin

doi:10.3389/fpls.2022.1088531

Cited by 17 publications

(2 citation statements)

References 34 publications

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“…In [64], a lightweight multi-scale fusion model (MSFM) that contain EfficientNet-B6 [53] as the base network is introduced. The pre-trained network incorporates CBAM prior to each regularization stage to improve the model's capacity to choose features.…”

Section: Msfmmentioning

confidence: 99%

3 The Imperial War Museum North: A Twenty-First Century Museum?

Bagnall¹,

Rowland²

2022

The Future of Memory

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This study is categorized into six sections, each serving a specific purpose. The initial section serves as an introduction, providing an overview of the main motivation and objectives of the study. Subsequently, in the second section, we discuss various attention mechanisms and their categorization. The third section explores different types of attentional mechanisms. In the fourth section, various studies proposed by researchers are examined, concentrating on leveraging attention mechanisms to improve the model's performance for efficient plant disease identification. Additionally, the fifth section conducts a comparative analysis between three deep learning models, both with and without the various attention modules. A discussion about the future potential of these mechanisms and the remaining challenges associated with their utilization is given. The study conclusion is presented in Section 6. Related workThis section includes the recent studies that have reviewed various attention mechanism. Study [3], conducted a thorough examination on attention mechanisms for computer vision task and categorized them based on their approach, encompassing temporal S. Duhan et al.

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Section: Msfmmentioning

confidence: 99%

3 The Imperial War Museum North: A Twenty-First Century Museum?

Bagnall¹,

Rowland²

2022

The Future of Memory

View full text Add to dashboard Cite

show abstract

“…In recent years, deep learning ( 6 ) has created a research boom in various fields. In the agricultural field, deep learning combined with machine vision has been widely used in plant recognition and detection, such as recognition of wood categories ( 7 ), fruit and vegetable classification ( 8–10 ), plant pest and disease identification ( 11 , 12 ), crop yield estimation ( 13 , 14 ) and weed detection ( 15 ).…”

Section: Introductionmentioning

confidence: 99%

Dried shiitake mushroom grade recognition using D-VGG network and machine vision

Wang,

Dong,

Wang

et al. 2023

Front. Nutr.

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Grading dried shiitake mushrooms is an indispensable production step, as there are large quality differences between different grades, which affect the product’s price and marketability. Dried shiitake mushroom samples have irregular shapes, small morphological differences between different grades of the same species, and they may occur in mixed grades, which causes challenges to the automatic grade recognition using machine vision. In this study, a comprehensive method to solve this problem is provided, including image acquisition, preprocessing, dataset creation, and grade recognition. The osprey optimization algorithm (OOA) is used to improve the computational efficiency of Otsu’s threshold binarization and obtain complete mushroom contours samples efficiently. Then, a method for dried shiitake mushroom grade recognition based on the improved VGG network (D-VGG) is proposed. The method uses the VGG16 network as the base framework, optimizes the convolutional layer of the network, and uses a global average pooling layer instead of a fully connected layer to reduce the risk of model overfitting. In addition, a residual module and batch normalization are introduced to enhance the learning effect of texture details, accelerate the convergence of the model, and improve the stability of the training process. An improved channel attention network is proposed to enhance the feature weights of different channels and improve the grading performance of the model. The experimental results show that the improved network model (D-VGG) can recognize different dried shiitake mushroom grades with high accuracy and recognition efficiency, achieving a final grading accuracy of 96.21%, with only 46.77 ms required to process a single image. The dried shiitake mushroom grade recognition method proposed in this study provides a new implementation approach for the dried shiitake mushroom quality grading process, as well as a reference for real-time grade recognition of other agricultural products.

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Cassava disease detection using a lightweight modified soft attention network

Dosset,

Dang,

Alharbi

et al. 2024

Pest Management Science

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BACKGROUNDCassava is a high‐carbohydrate crop that is at risk of viral infections. The production rate and quality of cassava crops are affected by several diseases. However, the manual identification of diseases is challenging and requires considerable time because of the lack of field professionals and the limited availability of clear and distinct information. Consequently, the agricultural management system is seeking an efficient and lightweight method that can be deployable to edged devices for detecting diseases at an early stage. To address these issues and accurately categorize different diseases, a very effective and lightweight framework called CDDNet has been introduced. We used MobileNetV3Small framework as a backbone feature for extracting optimized, discriminating, and distinct features. These features are empirically validated at the early intermediate stage. Additionally, we modified the soft attention module to effectively prioritize the diseased regions and enhance significant cassava plant disease‐related features for efficient cassava disease detection.RESULTSOur proposed method achieved accuracies of 98.95%, 97.03%, and 98.25% on Cassava Image Dataset, Cassava Plant Disease Merged (2019–2020) Dataset, and the newly created Cassava Plant Composite Dataset, respectively. Furthermore, the proposed technique outperforms previous state‐of‐the‐art methods in terms of accuracy, parameter count, and frames per second values, ultimately making the proposed CDDNet the best one for real‐time processing.CONCLUSIONOur findings underscore the importance of a lightweight and efficient technique for cassava disease detection and classification in a real‐time environment. Furthermore, we highlight the impact of modified soft attention on model performance. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism

Cited by 17 publications

References 34 publications

3 The Imperial War Museum North: A Twenty-First Century Museum?

3 The Imperial War Museum North: A Twenty-First Century Museum?

Dried shiitake mushroom grade recognition using D-VGG network and machine vision

Cassava disease detection using a lightweight modified soft attention network

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