Abstract
Real-time dynamic monitoring of orchard grape leaf diseases can greatly improve the efficiency of disease control and is of great significance to the healthy and stable development of the grape industry. Traditional manual disease-monitoring methods are inefficient, labor-intensive, and ineffective. Therefore, an efficient method is urgently needed for real-time dynamic monitoring of orchard grape diseases. The classical deep learning network can achieve high accuracy in recognizing grape leaf diseases; however, the large amount of model parameters requires huge computing resources, and it is difficult to deploy to actual application scenarios. To solve the above problems, a cross-channel interactive attention mechanism-based lightweight model (ECA-SNet) is proposed. First, based on 6,867 collected images of five common leaf diseases of measles, black rot, downy mildew, leaf blight, powdery mildew, and healthy leaves, image augmentation techniques are used to construct the training, validation, and test set. Then, with ShuffleNet-v2 as the backbone, an efficient channel attention strategy is introduced to strengthen the ability of the model for extracting fine-grained lesion features. Ultimately, the efficient lightweight model ECA-SNet is obtained by further simplifying the network layer structure. The model parameters amount of ECA-SNet 0.5× is only 24.6% of ShuffleNet-v2 1.0×, but the recognition accuracy is increased by 3.66 percentage points to 98.86%, and FLOPs are only 37.4 M, which means the performance is significantly better than other commonly used lightweight methods. Although the similarity of fine-grained features of different diseases image is relatively high, the average F1-score of the proposed lightweight model can still reach 0.988, which means the model has strong stability and anti-interference ability. The results show that the lightweight attention mechanism model proposed in this paper can efficiently use image fine-grained information to diagnose orchard grape leaf diseases at a low computing cost.
Highlights
Grape leaf disease is the main factor that causes a large-scale reduction in orchards and restricts the healthy and stable development of the grape industry
This section introduces the materials and methods used in the study, including the collected grape leaf disease image data, FGDs established through image enhancement techniques, relevant lightweight network, and detailed structure of the proposed model
In order to verify the performance of the efficient channel attention (ECA)-SNet network, the Python language is used to build a model based on the Pytorch 1.7.1 deep learning framework, and the model is trained and tested on a GPU-equipped server
Summary
Grape leaf disease is the main factor that causes a large-scale reduction in orchards and restricts the healthy and stable development of the grape industry. Ma J. et al (2018) proposed a deep convolutional neural network to identify three types of cucumber diseases and achieved an accuracy of 93.4%. Liu et al (2018) proposed a network based on AlexNet and GoogLeNet, which used deep learning to diagnose apple leaf diseases for the first time. Ferentinos (2018) tested five classical convolutional neural networks to identify plant leaf diseases, and the results showed that all of them can achieve ideal accuracy. The convolution neural network-based classification models mentioned above can achieve superior recognition results, it has the imperfection of highly dependent on the hardware performance of the device. The huge amount of network parameters leads to huge computational overhead, which cannot be afforded by ordinary devices, and it is difficult to deploy to the terminals for promotion
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