Wang Peng, Niu Tong, Mao Yanru, Liu Bin, Yang Shuqin, He Dongjian, Gao Qiang
College of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry (A&F) University, Yangling, China.
Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Xianyang, China.
Front Plant Sci. 2021 Oct 22;12:738042. doi: 10.3389/fpls.2021.738042. eCollection 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 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.
果园葡萄叶部病害的实时动态监测能够极大提高病害防治效率，对葡萄产业的健康稳定发展具有重要意义。传统的人工病害监测方法效率低下、劳动强度大且效果不佳。因此，迫切需要一种高效的方法来对果园葡萄病害进行实时动态监测。经典的深度学习网络在识别葡萄叶部病害方面能够达到较高的准确率；然而，大量的模型参数需要巨大的计算资源，并且难以部署到实际应用场景中。为了解决上述问题，提出了一种基于跨通道交互注意力机制的轻量级模型（ECA - SNet）。首先，基于收集到的6867张包含麻疹、黑腐病、霜霉病、叶枯病、白粉病这五种常见叶部病害以及健康叶片的图像，利用图像增强技术构建训练集、验证集和测试集。然后，以ShuffleNet - v2作为骨干网络，引入一种高效的通道注意力策略来增强模型提取细粒度病斑特征的能力。最终，通过进一步简化网络层结构得到高效轻量级模型ECA - SNet。ECA - SNet 0.5×的模型参数量仅为ShuffleNet - v2 1.0×的24.6%，但识别准确率提高了3.66个百分点，达到98.86%，且浮点运算次数仅为37.4M，这意味着其性能明显优于其他常用的轻量级方法。尽管不同病害图像的细粒度特征相似度相对较高，但所提出的轻量级模型的平均F1分数仍能达到0.988，这意味着该模型具有很强的稳定性和抗干扰能力。结果表明，本文提出的轻量级注意力机制模型能够以较低的计算成本高效利用图像细粒度信息诊断果园葡萄叶部病害。
