Attention embedded lightweight network for maize disease recognition

Chen, Junde; Wang, Wenhua; Zhang, Defu; Zeb, Adnan; Nanehkaran, Yaser Ahangari

doi:10.1111/ppa.13322

Cited by 77 publications

(37 citation statements)

References 24 publications

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“…e accuracy of the model in the classification of cotton leaf diseases and insect pests was 96.4%. Chen et al [10] proposed a new network architecture, Mobile-DANet, to identify maize diseases, and the model achieved an average accuracy of 98.50% on the open maize data set.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

Yin-e

Liu

2021

Computational Intelligence and Neuroscience

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The prevention and control of navel orange pests and diseases is an important measure to ensure the yield of navel oranges. Aiming at the problems of slow speed, strong subjectivity, high requirements for professional knowledge required, and high identification costs in the identification methods of navel orange pests and diseases, this paper proposes a method based on DenseNet and attention. The power mechanism fusion (DCPSNET) identification method of navel orange diseases and pests improves the traditional deep dense network DenseNet model to realize accurate and efficient identification of navel orange diseases and pests. Due to the difficulty in collecting data of navel orange pests and diseases, this article uses image enhancement technology to expand. The experimental results show that, in the case of small samples, compared with the traditional model, the DCPSNET model can accurately identify different types of navel orange diseases and pests images and the accuracy of identifying six types of navel orange diseases and pests on the test set is as high as 96.90%. The method proposed in this paper has high recognition accuracy, realizes the intelligent recognition of navel orange diseases and pests, and also provides a way for high-precision recognition of small sample data sets.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

Yin-e

Liu

2021

Computational Intelligence and Neuroscience

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“…In this paper, unlike the traditional research that only uses lightweight models [20][21][22][23]47], we combine pruning, distillation, and quantization methods to minimize the size of the model while ensuring accuracy. Compared with existing lightweight models, our proposed model compression method on VGGNet and AlexNet yields more competitive results (see Section 4.3 for details).…”

Section: Architectures For Plant Disease Detectionmentioning

confidence: 99%

“…Researchers in [19] deployed a lightweight neural network after knowledge distillation on an agricultural robot platform to distinguish between weeds and crops. In [20][21][22][23], authors used lightweight CNNs to identify diseased crop leaves for easier deployment on embedded devices. However, in a related study of model compression [24], a lightweight network was only one part of the solution.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Compression for Plant Disease Recognition

Wang

Ding

et al. 2021

Symmetry

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Deep neural networks (DNNs) have become the de facto standard for image recognition tasks, and their applications with respect to plant diseases have also obtained remarkable results. However, the large number of parameters and high computational complexities of these network models make them difficult to deploy on farms in remote areas. In this paper, focusing on the problems of resource constraints and plant diseases, we propose a DNN-based compression method. In order to reduce computational burden, this method uses lightweight fully connected layers to accelerate reasoning, pruning to remove redundant parameters and reduce multiply–accumulate operations, knowledge distillation instead of retraining to restore the lost accuracy, and then quantization to compress the size of the model further. After compressing the mainstream VGGNet and AlexNet models, the compressed versions are applied to the Plant Village dataset of plant disease images, and a performance comparison of the models before and after compression is obtained to verify the proposed method. The results show that the model can be compressed to 0.04 Mb with an accuracy of 97.09%. This experiment also proves the effectiveness of knowledge distillation during the pruning process, and compressed models are more efficient than prevalent lightweight models.

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“…In the maize crop, a few but significant works have been done for automatic identification of several diseases 42 – 49 . The authors 44 – 46 have worked on developing deep learning models for identifying diseases of maize crop.…”

Section: Introductionmentioning

confidence: 99%

“…However, a limitation of these works is that in both studies only one disease of maize has been addressed. Chen et al 49 proposed a lightweight network for recognition of eight maize diseases. They incorporated attention module with the DenseNet architecture to propose the novel model.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based approach for identification of diseases of maize crop

Haque

Marwaha

Deb

et al. 2022

Sci Rep

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In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR-IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non-destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception-v3’ network were trained with the collected diseased images of maize using baseline training approach. The best-performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best-performing model with some pre-trained state-of-the-art models and presented the comparative results in this manuscript. The results reported that best-performing model performed quite better than the pre-trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best-performed model is efficient in recognizing diseases of maize from in-field images even with varied backgrounds.

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Attention embedded lightweight network for maize disease recognition

Cited by 77 publications

References 24 publications

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

[Retracted] Identification of Navel Orange Diseases and Pests Based on the Fusion of DenseNet and Self‐Attention Mechanism

Deep Neural Network Compression for Plant Disease Recognition

Deep learning-based approach for identification of diseases of maize crop

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