Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

Wang, Peng; Niu, Tong; Mao, Yanru; Zhang, Zhao; Liu, Bin; He, Dongjian

doi:10.3389/fpls.2021.723294

Cited by 42 publications

(28 citation statements)

References 32 publications

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“…The architecture outperformed conventional state-of-the-art deep learning models. Wang et al ( 2021 ) introduced a deep learning model with an attention mechanism, called the Coordination Attention EfficientNet, to identify different apple leaf diseases. The coordinate attention block was embedded into the novel deep convolutional neural network and extracted important channel features and spatial location information.…”

Section: Related Workmentioning

confidence: 99%

“…To compensate for the accuracy loss of lightweight models, attention mechanisms can be used to distribute different weights to each part of the input feature layers, extract essential features, and improve classification performance. In addition, attention mechanisms have little impact on efficiency and do not require large storage space for the model (Wang et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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MGA-YOLO: A lightweight one-stage network for apple leaf disease detection

Wang

Zhao

2022

Front. Plant Sci.

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Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms have been proposed to diagnose apple leaf diseases, but most of them are designed to run on high-performance GPUs. This potentially limits their application in the field, in which mobile devices are expected to be used to perform computer vision-based disease diagnosis on the spot. In this paper, we propose a lightweight one-stage network, called the Mobile Ghost Attention YOLO network (MGA-YOLO), which enables real-time diagnosis of apple leaf diseases on mobile devices. We also built a dataset, called the Apple Leaf Disease Object Detection dataset (ALDOD), that contains 8,838 images of healthy and infected apple leaves with complex backgrounds, collected from existing public datasets. In our proposed model, we replaced the ordinary convolution with the Ghost module to significantly reduce the number of parameters and floating point operations (FLOPs) due to cheap operations of the Ghost module. We then constructed the Mobile Inverted Residual Bottleneck Convolution and integrated the Convolutional Block Attention Module (CBAM) into the YOLO network to improve its performance on feature extraction. Finally, an extra prediction head was added to detect extra large objects. We tested our method on the ALDOD testing set. Results showed that our method outperformed other state-of-the-art methods with the highest mAP of 89.3%, the smallest model size of only 10.34 MB and the highest frames per second (FPS) of 84.1 on the GPU server. The proposed model was also tested on a mobile phone, which achieved 12.5 FPS. In addition, by applying image augmentation techniques on the dataset, mAP of our method was further improved to 94.0%. These results suggest that our model can accurately and efficiently detect apple leaf diseases and can be used for real-time detection of apple leaf diseases on mobile devices.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MGA-YOLO: A lightweight one-stage network for apple leaf disease detection

Wang

Zhao

2022

Front. Plant Sci.

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“…When collecting the images for the dataset, some of them were taken in a group; cropping of those images increased the size of the dataset. Furthermore, several data augmentation techniques were applied, such as a 30% increase and decrease in brightness, contrast, and sharpness [25]. Moreover, two noises are injected into the training images to study their effects and increase the variability in the dataset.…”

Section: ) Data Augmentation Techniquesmentioning

confidence: 99%

“…A few studies have also proposed real-time detection of plant disease. A DL model was presented for the identification of tomato VOLUME XX, 2022 disease [25]. Similarly, disease detection on grape leaves was performed using a DL architecture based on Faster regionbased convolutional neural network (R-CNN) [26].…”

Section: Introductionmentioning

confidence: 99%

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

2022

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Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After final the most suitable DL model, data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.

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“… Sardogan et al (2020) constructed an Incept-Faster-RCNN model, which introduced the Inception v2 structure to the Faster-RCNN model for the detection of two apple leaf diseases; its detection accuracy was 84.50%. Wang et al (2021) proposed a deep learning model (CA-ENet) for identifying apple diseases, which applied deep separable convolution to reduce the number of parameters and obtained good recognition results.…”

Section: Introductionmentioning

confidence: 99%

Lightweight-Convolutional Neural Network for Apple Leaf Disease Identification

Sun

et al. 2022

Front. Plant Sci.

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As a widely consumed fruit worldwide, it is extremely important to prevent and control disease in apple trees. In this research, we designed convolutional neural networks (CNNs) for five diseases that affect apple tree leaves based on the AlexNet model. First, the coarse-grained features of the disease are extracted in the model using dilated convolution, which helps to maintain a large receptive field while reducing the number of parameters. The parallel convolution module is added to extract leaf disease features at multiple scales. Subsequently, the series 3 × 3 convolutions shortcut connection allows the model to deal with additional nonlinearities. Further, the attention mechanism is added to all aggregated output modules to better fit channel features and reduce the impact of a complex background on the model performance. Finally, the two fully connected layers are replaced by global pooling to reduce the number of model parameters, to ensure that the features are not lost. The final recognition accuracy of the model is 97.36%, and the size of the model is 5.87 MB. In comparison with five other models, our model design is reasonable and has good robustness; further, the results show that the proposed model is lightweight and can identify apple leaf diseases with high accuracy.

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Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks With an Attention Mechanism

Cited by 42 publications

References 32 publications

MGA-YOLO: A lightweight one-stage network for apple leaf disease detection

MGA-YOLO: A lightweight one-stage network for apple leaf disease detection

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

Lightweight-Convolutional Neural Network for Apple Leaf Disease Identification

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