Identifying crop diseases using attention embedded MobileNet-V2 model

Chen, Junde; Zhang, Defu; Suzauddola,; Zeb, Adnan

doi:10.1016/j.asoc.2021.107901

Cited by 61 publications

(26 citation statements)

References 27 publications

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“…Subsequently, with the increasing difficulty of the classification task, the improved accuracy of the attention mechanism was reduced. Furthermore, the existing models ignored the loss of feature information during feature extraction [18,19]. Thus, we designed a module to reduce the loss of feature information, namely the RFFB module.…”

Section: Discussionmentioning

confidence: 99%

“…Gao et al proposed a dual-branch, efficient, channel attention (DECA)-based crop disease recognition model, and the recognition accuracy of the model was 86.65%, 99.74%, and 98.54% on the datasets of PlantVillage, AI Challenger 2018, and Cucumber disease, respectively [18]. Chen et al introduced the Location-wise Soft Attention mechanism to the pre-trained MobileNetV2 [19]. Furthermore, a two-phase progressive strategy was executed for model training.…”

Section: Introductionmentioning

confidence: 99%

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GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, the differences in symptoms between early crop disease and late crop disease stages include the area of disease and color of disease. This also poses additional difficulties for CNN models. Here, we propose a lightweight CNN model called GrapeNet for the identification of different symptom stages for specific grape diseases. The main components of GrapeNet are residual blocks, residual feature fusion blocks (RFFBs), and convolution block attention modules. The residual blocks are used to deepen the network depth and extract rich features. To alleviate the CNN performance degradation associated with a large number of hidden layers, we designed an RFFB module based on the residual block. It fuses the average pooled feature map before the residual block input and the high-dimensional feature maps after the residual block output by a concatenation operation, thereby achieving feature fusion at different depths. In addition, the convolutional block attention module (CBAM) is introduced after each RFFB module to extract valid disease information. The obtained results show that the identification accuracy was determined as 82.99%, 84.01%, 82.74%, 84.77%, 80.96%, 82.74%, 80.96%, 83.76%, and 86.29% for GoogLeNet, Vgg16, ResNet34, DenseNet121, MobileNetV2, MobileNetV3_large, ShuffleNetV2_×1.0, EfficientNetV2_s, and GrapeNet. The GrapeNet model achieved the best classification performance when compared with other classical models. The total number of parameters of the GrapeNet model only included 2.15 million. Compared with DenseNet121, which has the highest accuracy among classical network models, the number of parameters of GrapeNet was reduced by 4.81 million, thereby reducing the training time of GrapeNet by about two times compared with that of DenseNet121. Moreover, the visualization results of Grad-cam indicate that the introduction of CBAM can emphasize disease information and suppress irrelevant information. The overall results suggest that the GrapeNet model is useful for the automatic identification of grape leaf diseases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

et al. 2022

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“…As you can see in Table 1 , the state-of-the-art plant diseases models achieve a very high classification accuracy, requiring a large number of parameters and higher computation cost, which prohibit their usage in embedded devices. In recent years the models have achieved very high accuracies, exploring different methods: transfer learning applied to existing architectures in literature [4] , [21] , [8] , [23] , [27] , existing DNN models combined with different features extraction methods [11] , [17] , modified versions of existing networks [31] , [33] , [41] , [36] , novel network architectures [25] , [29] , [34] , [38] , [39] , [40] . Such a higher accuracy, in most cases, has been reached using complexed architectures that require a high number of parameters.…”

Section: Hardware In Contextmentioning

confidence: 99%

A CNN-based image detector for plant leaf diseases classification

Falaschetti¹,

Manoni²,

Leo³

et al. 2022

HardwareX

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“…The authors in [34] simulated optical light reflection and performed alignment of the crystalized region. In [35], the proposed system used a two-camera estimation system to perform automated detection. Later, in [36], the authors developed a convolutional neural network to generate features on the basis of the diffraction peaks in plant electron micrographs, finally leading to the ability to the recognize calcium oxalate crystals.…”

Section: Electron Micrograph Processing For the Detection Of Calcium ...mentioning

confidence: 99%

Computerized Detection of Calcium Oxalate Crystal Progression

Mahmoud

2022

Crystals

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Calcium oxalate crystals in plants can cause health issues such as kidney stones if ingested in large amounts. Calcium oxalate crystallizations affect approximately 4% of plants. Some of these crystallizations are more common, and human and animal ingestion can be avoided if the degree of severity is detected at an early stage. Therefore, in this paper, we present a computerized method for detecting calcium oxalate crystallizations at an early stage, when chances for avoiding it are higher. In our research, electron micrograph processing techniques are used to extract features and measure the degree of crystallization progression in cases of crystalized plants and normal plants. A new fast search algorithm—ODS: One Direction Search—is proposed to detect calcium oxalate crystal progression. The calcium oxalate crystal progression is detected on the basis of electron micrographs of calcium oxalate crystals by means of a temporal test. We employed deep learning for feature extraction. The deep learning technique uses transfer learning, which allows the proposed detection model to be trained on only a small amount of data regarding calcium oxalate crystals for the determination of the presence of calcium oxalate crystals and the severity of the cases. The experimental results, using electron micrographs of 6900 clusters, demonstrated a success rate of 97.5% when detecting cases of calcium oxalate crystals. The simulation results of the new temporal algorithm show an enhancement of the speed by 70% compared to well-known temporal algorithms, and increased accuracy when computing PRSN against other algorithms.

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Identifying crop diseases using attention embedded MobileNet-V2 model

Cited by 61 publications

References 27 publications

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases

A CNN-based image detector for plant leaf diseases classification

Computerized Detection of Calcium Oxalate Crystal Progression

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