Zhang Zhao, Qiao Yongliang, Guo Yangyang, He Dongjian
College of Mechanical and Electronic Engineering, Northwest A&F University, Xianyang, China.
College of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji, China.
Front Plant Sci. 2022 Jun 9;13:872107. doi: 10.3389/fpls.2022.872107. eCollection 2022.
Grape downy mildew (GDM) disease is a common plant leaf disease, and it causes serious damage to grape production, reducing yield and fruit quality. Traditional manual disease detection relies on farm experts and is often time-consuming. Computer vision technologies and artificial intelligence could provide automatic disease detection for real-time controlling the spread of disease on the grapevine in precision viticulture. To achieve the best trade-off between GDM detection accuracy and speed under natural environments, a deep learning based approach named YOLOv5-CA is proposed in this study. Here coordinate attention (CA) mechanism is integrated into YOLOv5, which highlights the downy mildew disease-related visual features to enhance the detection performance. A challenging GDM dataset was acquired in a vineyard under a nature scene (consisting of different illuminations, shadows, and backgrounds) to test the proposed approach. Experimental results show that the proposed YOLOv5-CA achieved a detection precision of 85.59%, a recall of 83.70%, and a mAP@0.5 of 89.55%, which is superior to the popular methods, including Faster R-CNN, YOLOv3, and YOLOv5. Furthermore, our proposed approach with inference occurring at 58.82 frames per second, could be deployed for the real-time disease control requirement. In addition, the proposed YOLOv5-CA based approach could effectively capture leaf disease related visual features resulting in higher GDE detection accuracy. Overall, this study provides a favorable deep learning based approach for the rapid and accurate diagnosis of grape leaf diseases in the field of automatic disease detection.
葡萄霜霉病（GDM）是一种常见的植物叶片病害，对葡萄生产造成严重损害，降低产量和果实品质。传统的人工病害检测依赖于农场专家，且往往耗时较长。计算机视觉技术和人工智能可为精准葡萄栽培中实时控制葡萄霜霉病在葡萄藤上的传播提供自动病害检测。为在自然环境下实现GDM检测精度和速度的最佳平衡，本研究提出了一种基于深度学习的方法YOLOv5-CA。在此，坐标注意力（CA）机制被集成到YOLOv5中，突出与霜霉病相关的视觉特征以提高检测性能。在自然场景下的葡萄园（包含不同光照、阴影和背景）中获取了具有挑战性的GDM数据集，以测试所提方法。实验结果表明，所提的YOLOv5-CA实现了85.59%的检测精度、83.70%的召回率和89.55%的mAP@0.5，优于包括Faster R-CNN、YOLOv3和YOLOv5在内的流行方法。此外，我们所提方法的推理速度为每秒58.82帧，可满足实时病害控制需求。此外，所提基于YOLOv5-CA的方法能够有效捕捉与叶片病害相关的视觉特征，从而提高GDE检测精度。总体而言，本研究为自动病害检测领域中葡萄叶片病害的快速准确诊断提供了一种良好的基于深度学习的方法。
