Grape as an important fruit crop makes a large economic income contribution in many countries (Liu et al., 2020; Zhou et al., 2021). As the grape grows in a natural condition, diseases will often appear on the leaves due to the complex weather condition and changing surrounding environments. Grape downy mildew (GDM) is one of the serious diseases caused by the oomycete pathogen Plamopara viticola, which seriously affects the growth of the grapes, causes a decrease in quality and yield, and results in huge economic losses in the grape industry (Chen et al., 2020; Ji et al., 2020). Downy mildew often happened in wet and rainy areas in spring and summer, it is initiated at the stomata on the underside of the leaf, and then on the whole leaf (Chen et al., 2020). Monitoring grape leaf health and detecting pathogen are essential to reduce disease spread and facilitate effective management practices. Grape leaf diseases are currently controlled by repetitive fungicide treatments throughout the season. Reducing the treatment costs is a major challenge from both environmental and economic views. Timely detection and treatment at the initial stage of downy mildew infection (Adeel et al., 2019) is a good solution to control and cut down the spread of downy mildew in a large area. Therefore, if an automatic detection method can be achieved when the spots appear, the leaf disease control plan can be made to control the diseases, guarantee the grape plant health, and improve the quality and yield of the grapes. Vision based detection approaches have been developed to detect plant diseases, which is performed by extracting visual features (e.g., texture, shape, and color of leaf lesions) of leaf images and using models (e.g., support vector machine, linear regression) to recognize and detect the diseases (Tang et al., 2020; Hernández et al., 2021). Zhu et al. (2020) identified grape diseases using image analysis and BP neural networks. Chen et al. (2020) developed and compared several generalized linear models to predict the probability of high incidence and severity in the Bordeaux vineyard region. Abdelghafour et al. (2020) detected downy mildew symptoms using proximal color imaging and achieved 83% pixel-wise precision. However, the traditional image processing technology needs to manually extract the leaf disease characteristics, which is often time-consuming, and easy to miss the best disease prevention time. In addition, under the nature scene (e.g., different illumination, symptoms, camera viewpoints), classical algorithms or models lack robustness and cannot achieve stable detect performance.

Many scholars have proposed approaches for earlier plant disease detection and monitoring of the disease symptoms (Mutka and Bart, 2015). At the earlier stage, the human-crafted features such as texture, color, or shape characteristics are extracted from RGB or hyper-spectral plant leaf images for identifying the plant diseases (Mahlein, 2016). For example, Atanassova et al. (2019) proposed spectral data based classification models to predict the infection in plants, which achieved over 78% accuracy. Waghmare et al. (2016) proposed an automatic grape diseases detection system using the extracted color Local Binary Pattern (LBP) features. Mohammadpoor et al. (2020) proposed a support vector machine for grape fanleaf virus detection and achieved 98.6% average accuracy. However, this kind of method mostly depends on selected features and their extraction is easy to be influenced by the camera viewpoints, shadows, and lighting.

In recent years, deep learning methods such as convolutional neural networks (CNN) have been widely implemented in leaf disease detection, scene perception, and smart agriculture. Variates CNN based detection methods have been proposed for leaf disease recognition and monitoring (Liu et al., 2020). Ferentinos (2018) proposed convolutional neural network architectures to identify healthy or diseased plants. Arsenovic et al. (2019) developed a two-stage architecture of neural networks to classify plant disease and achieved an accuracy of 93.67%. Zhang et al. (2019a) proposed an AlexNet based cucumber disease identification approach, achieving 94.65% recognition accuracy. Ji et al. (2020) proposed CNN based approach to classify common grape leaf diseases and obtained average classification accuracy of 98.57%. Liu et al. (2020) proposed Inception convolutional neural network (DICNN) for identifying grape leaf diseases and realized an overall accuracy of 97.22% on single-leaf datasets. Thet et al. (2020) used an improved VGG16 model that achieved 98.4% classification accuracy for five different leaf diseases. Tang et al. (2020) classified grape disease types using lightweight convolution neural networks and channel-wise attention, which achieved 99.14% accuracy. Liu and Wang (2020) improved the YOLOv3 model to directly generate the bounding box coordinates for tomato diseases and pests detection, which achieved a detection accuracy of 92.39%. According to these studies, CNNs can learn advanced robust features of leaf diseases directly from original images, outperforming the traditional feature extraction approaches. Yu and Son (2020) proposed a leaf spot attention mechanism to increase apple leaf disease discriminative power and enhance the identification performance. Hernández and Lopez (2020) developed Bayesian deep learning techniques and an uncertainty probabilistic programming approach for plant disease detection.

With the continuous development of smart sensors, big data, and cloud computing, many automatic approaches have been proposed to identify and detect plant leaf diseases (Vishnoi et al., 2021). The rapid development of artificial intelligence and the Internet of Things (IoT) has significantly facilitated automatic disease detection (Zhang et al., 2020). Using deep learning models and noninvasive sensors to identify plant diseases has drawn more attention in the field of precision agriculture and plant phenotyping (Nagaraju and Chawla, 2020; Singh et al., 2020). Hernández et al. (2021) investigated hyperspectral sensing technologies and artificial intelligence applications for assessing downy mildew in grapevine under laboratory conditions. Gutiérrez et al. (2021) differentiated downy mildew and spider mite in grapevine under field conditions using the CNN model. Liu et al. (2021) proposed Hierarchical Multi-Scale Attention Semantic Segmentation (HMASS) to identify GDM infected regions, and the calculated infection severity percentage was highly correlated (R = 0.96) with the human field assessment.

Choi and Hsiao (2021) classified Cassava leaf diseases using the Residual Network. Zhang et al. (2021) developed a multi-feature fusion Faster R-CNN model and achieved 83.34% detection accuracy for soybean leaf disease. Dinata et al. (2021) proposed CNN based approach for 6 types of strawberry disease classification and achieved 63.7% accuracy. Abbas et al. (2021) detected tomato plant disease using transfer learning and C-GAN synthetic images, which achieved 99.51% accuracy. Cristin et al. (2020) proposed a deep neural network based Rider-Cuckoo Search Algorithm and achieved 87.7% plant disease detection accuracy. Roy and Bhaduri (2021) proposed deep learning-based multi-class plant disease and achieved 91.2% mean average precision. However, most of these methods are only tested in experimental situations, which need to be verified on the complex background situation.

Despite deep learning based approaches demonstrating its facilitate in GDM detection, the detection accuracy and speed restricted its application in autonomous viticulture management. Plant leaf disease detection in the real vineyard is facing many challenges, such as the small difference between the lesion area and the background, different scales of the spots, variation of symptoms, and camera viewpoints (Liu and Wang, 2021). Also, light changing in a real complex natural environment further increased the difficulty to achieve high detection accuracy. Therefore, real-time and accurate detection of grape downy mildew is of great significance for the scientific management and control of grape diseases in precision vineyard farming.

Recently, the attention mechanisms such as Squeeze-and-Excitation Networks (SE) (Hu et al., 2018), Convolutional block attention module (CBAM) (Woo et al., 2018), and CA (Hou et al., 2021) have been widely used to enhance the deep learning model performances. SE simply squeezes each 2D feature map to efficiently build interdependencies among channels (Hu et al., 2018). CBAM introduces spatial information encoding via convolutions with large-size kernels and gathers channel-wise and spatial-wise attention sequentially. The recently proposed CA adopts different spatial attention mechanisms and designs advanced attention blocks. Zhang et al. (2019b) applied an attention mechanism to object detection networks, enhancing the impact of significant features and weakening background interference. Experimental results show that the proposed approach achieved an object detection accuracy of 75.9% on PASCAL VOC 2007, which is 6% higher than Faster R-CNN. Liu et al. (2019) presented a deep neural network architecture based on information transmission and attention mechanisms. Zhao et al. (2021) diagnosed tomato leaf disease using an attention module improved network, which achieved 96.81% average identification accuracy on the tomato leaf diseases dataset. Ravi et al. (2021) integrated the attention module into the EfficientNet model to locate and identify the tiny infected regions in the Cassava leaf. The proposed method achieved better performance than non-attention-based CNN pre-trained models. Wang et al. (2021b) proposed a Fine-Grained grape leaf disease recognition method using a lightweight attention network, which can efficiently diagnose orchard grape leaf diseases with low computing cost. The above studies have demonstrated that attention mechanisms could enhance feature extraction ability for leaf disease detection and identification.

In this study, to improve GDM detection accuracy in the natural grape farm environment, we proposed YOLOv5-CA based GDM detection approach by combing YOLOv5 and CA mechanism. Different scales of image features were extracted through CNN layers of YOLOv5, and these features were weighted by CA for GDM detection. By using CA, the features' effectiveness for GDM detection is highlighted and those less effective features are suppressed. The proposed YOLOv5-CA based GDM detection is tested on our acquired grape leaf image dataset.

The remaining part of the article is organized as follows. Section 2 illustrates the used datasets, the proposed approaches, and evaluation indicators. Experimental results are presented in Section 3. Discussions of the performance are presented in Section 4. Finally, conclusions and future areas for research are given in Section 5.

This study presents a deep learning-based pipeline for automatic GDM detection in the vineyard. The grape leaf images acquired directly from the plants under field conditions were used to verify our proposed YOLOv5-CA approach. According to our experimental results presented in Table 1, a precision of 85.59%, a recall of 83.70%, an F₁-score of 84.63%, and a mAP@0.5 of 89.55% with the inference speed of 58.82 frames per second (FPS) was obtained for GDM detection. The detection accuracy of the proposed YOLOv5-CA is superior to that of state-of-the-art methods such as Faster R-CNN, YOLOv4, and YOLOv5. This high accuracy demonstrates the effectiveness of YOLOv5-CA for GDM detection of grapevine leaf images taken under field conditions. There yield results show that it is feasible to model visual symptoms for automatic GDM detection using a combination of the YOLOv5 and the CA mechanism. The proposed YOLOv5-CA automatically finds complex features capable of differentiating leaves with downy mildew symptoms and without any, which provides a precise and effective method for automatic disease detection.

On the other hand, the results presented in Table 2 reveal the appropriate network input size in our experiments is 416 ×416. Additionally, Table 3 compared the GDM detection performance with and without data augmentation, it shows that data augmentation enhances the GDM detection performance. The possible reason is that data augmentation increases the size of the dataset and brings more diversity to leverage the model training.

Although this study mainly focuses on GDM detection, it is suitable for multi-diseases detection (e.g., black spot, powdery mildew) after the model was re-trained with the dataset containing these diseases. As our approach uses RGB images, it would be a restriction for detecting GDM in the very earlier stage (i.e., non-visible symptoms) detection. However, if the multi-spectral images were acquired and used, our proposed YOLOv5-CA could be a potential tool to distinguish downy mildew from other leaf diseases/damage.

To achieve an accurate and real-time intelligent detection of GDM under natural environments, an automatic YOLOv5-CA based detection method was proposed in this study. By combing YOLOv5 and coordinate attention, the GDM related visual features are well focused on and extracted, which boosts the GDM detection performance. Our proposed YOLOv5-CA achieved 85.59% detection precision, 89.55% mAP@0.5 with 58.82 FPS, which outperformed Faster R-CNN, YOLOv4, and YOLOv5. Moreover, the test results showed that the different disease levels of GDM and the illumination influence would not have a great impact on the GDM detection results, indicating the proposed method is feasible for the rapid and accurate detection of GDM. Ablation studies show that a network input size of 416 ×416 is favorable for fast GDM detection, and bounding box-based data augmentation boosts the GDM detection precision by 3.23%. The results exposed in this work indicate that downy mildew in grapevine can be automatically evaluated using artificial intelligence technology.

Overall, our approach achieved a good trade-off between speed and accuracy for GDM, and can be adapted to applications with autonomous-based smart farming. For future study, the multi-spectral information and edge computing will be exploited to further improve detection performance and computational efficiency.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

ZZ: investigation, methodology, writing-review, and editing. YQ: data curation, methodology, formal analysis, and writing-original draft. YG: writing-review and editing. DH: resources and article revising. All authors contributed to the article and approved the submitted version.

This research was funded by the National Key Research and Development Program of China (2019YFD1002500), Ningxia Hui Autonomous Region Key Research and Development Program (2021BEF02015), and Ningxia Hui Autonomous Region Flexible Introduction Team Project (2020RXTDLX08).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.