Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Chen, Riyao; Huang, Qi; Yu, Liang; Ming-chao, Yang

doi:10.3389/fpls.2022.1023515

Cited by 20 publications

(16 citation statements)

References 41 publications

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“…Among them, the performance of CACPNET is closest to our method. CACPNET further reduces the model’s complexity and memory size based on channel pruning ( Chen et al., 2022b ). Channel pruning is a highly challenging task that requires calculating the weights of each channel and sorting them, as well as a certain degree of manual adjustment to achieve the desired performance.…”

Section: Resultsmentioning

confidence: 99%

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MS-Net: a novel lightweight and precise model for plant disease identification

Quan,

Wang,

Jia

et al. 2023

Front. Plant Sci.

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The rapid development of image processing technology and the improvement of computing power in recent years have made deep learning one of the main methods for plant disease identification. Currently, many neural network models have shown better performance in plant disease identification. Typically, the performance improvement of the model needs to be achieved by increasing the depth of the network. However, this also increases the computational complexity, memory requirements, and training time, which will be detrimental to the deployment of the model on mobile devices. To address this problem, a novel lightweight convolutional neural network has been proposed for plant disease detection. Skip connections are introduced into the conventional MobileNetV3 network to enrich the input features of the deep network, and the feature fusion weight parameters in the skip connections are optimized using an improved whale optimization algorithm to achieve higher classification accuracy. In addition, the bias loss substitutes the conventional cross-entropy loss to reduce the interference caused by redundant data during the learning process. The proposed model is pre-trained on the plant classification task dataset instead of using the classical ImageNet for pre-training, which further enhances the performance and robustness of the model. The constructed network achieved high performance with fewer parameters, reaching an accuracy of 99.8% on the PlantVillage dataset. Encouragingly, it also achieved a prediction accuracy of 97.8% on an apple leaf disease dataset with a complex outdoor background. The experimental results show that compared with existing advanced plant disease diagnosis models, the proposed model has fewer parameters, higher recognition accuracy, and lower complexity.

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Section: Resultsmentioning

confidence: 99%

“…With a model size of 19.1 MB, they achieved a classification accuracy of 99.55%. Chen et al. (2022b) proposed an improved ResNet-18 method for disease recognition in peanut leaf datasets and PlantVillage datasets.…”

Section: Related Workmentioning

confidence: 99%

MS-Net: a novel lightweight and precise model for plant disease identification

Quan,

Wang,

Jia

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

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“…It allows us to drastically lower the computations to get output feature maps. It also enables efficient data and model parallelism, which obviously benefits faster convergence, compared to the methods proposed by Chen et al. (2022) and Liu and Zhang (2022) .…”

Section: Discussionmentioning

confidence: 99%

A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity

Qu,

Zheng,

et al. 2024

Front. Plant Sci.

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IntroductionMummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult.MethodsUsing real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate.ResultsExperimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16.DiscussionThese features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification.

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“…This model allows to focus on all parts of the input image according to their importance for the current task, thus capturing long-term dependencies in this data. ViT has demonstrated impressive performance in recent years in many computer vision tasks, especially in leaf disease detection and classification [19], [6], [20], [7]. ViT offers the ability to process high resolution images with high levels of detail, learn global context, and be pre-trained on large amounts of data.…”

Section: Detection Of Crop Diseasesmentioning

confidence: 99%

Image-based crop disease detection with federated learning

KABALA,

HAFIANE,

BOBELIN

et al. 2023

Preprint

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Crop disease detection and management is critical to improving productivity, reducing costs, and promoting environmentally friendly crop treatment methods. Modern technologies, such as data mining and machine learning algorithms, have been used to develop automated crop disease detection systems. However, centralized approach to data collection and model training induces challenges in terms of data privacy, availability, and transfer costs. To address these challenges, federated learning appears to be a promising solution. In this paper, we explored the application of federated learning for crop disease classification using image analysis. We developed and studied convolutional neural network (CNN) models and those based on attention mechanisms, in this case vision transformers (ViT), using federated learning, leveraging an open access image dataset from the "PlantVillage" platform. Experiments conducted concluded that the performance of models trained by federated learning is influenced by the number of learners involved, the number of communication rounds, the number of local iterations and the quality of the data. With the objective of highlighting the potential of federated learning in crop disease classification, among the CNN models tested, ResNet50 performed better in several experiments than the other models, and proved to be an optimal choice, but also the most suitable for a federated learning scenario. The ViT_B16 and ViT_B32 Vision Transformers require more computational time, making them less suitable in a federated learning scenario, where computational time and communication costs are key parameters. The paper provides a state-of-the-art analysis, presents our methodology and experimental results, and concludes with ideas and future directions for our research on using federated learning in the context of crop disease classification.

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Identification of plant leaf diseases by deep learning based on channel attention and channel pruning

Cited by 20 publications

References 41 publications

MS-Net: a novel lightweight and precise model for plant disease identification

MS-Net: a novel lightweight and precise model for plant disease identification

A deep multi-task learning approach to identifying mummy berry infection sites, the disease stage, and severity

Image-based crop disease detection with federated learning

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