Fine-Grained Grape Leaf Diseases Recognition Method Based on Improved Lightweight Attention Network - Wang, Peng; Niu, Tong; Mao, Yanru; Liu, Bin; Yang, Shuqin; He, Dongjian; Gao, Qiang (Frontiers in Plant Science, 2021)
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 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 on recognize 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 above problems, a cross-channel interactive attention mechanism based lightweight model (ECA-SNet) is proposed. First, based on 6867 collected images of 5 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 model's ability 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 37.4M, 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 reaches 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.