Li Kaiyu, Zhang Lingxian, Li Bo, Li Shufei, Ma Juncheng
College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.
Key Laboratory of Agricultural Informationization Standardization, Ministry of Agriculture and Rural Affairs, Beijing, 100083, China.
Plant Methods. 2022 Sep 6;18(1):109. doi: 10.1186/s13007-022-00941-8.
Automatic and accurate estimation of disease severity is critical for disease management and yield loss prediction. Conventional disease severity estimation is performed using images with simple backgrounds, which is limited in practical applications. Thus, there is an urgent need to develop a method for estimating the disease severity of plants based on leaf images captured in field conditions, which is very challenging since the intensity of sunlight is constantly changing, and the image background is complicated.
This study developed a simple and accurate image-based disease severity estimation method using an optimized neural network. A hybrid attention and transfer learning optimized semantic segmentation model was proposed to obtain the disease segmentation map. The severity was calculated by the ratio of lesion pixels to leaf pixels. The proposed method was validated using cucumber downy mildew, and powdery mildew leaves collected under natural conditions. The results showed that hybrid attention with the interaction of spatial attention and channel attention can extract fine lesion and leaf features, and transfer learning can further improve the segmentation accuracy of the model. The proposed method can accurately segment healthy leaves and lesions (MIoU = 81.23%, FWIoU = 91.89%). In addition, the severity of cucumber leaf disease was accurately estimated (R = 0.9578, RMSE = 1.1385). Moreover, the proposed model was compared with six different backbones and four semantic segmentation models. The results show that the proposed model outperforms the compared models under complex conditions, and can refine lesion segmentation and accurately estimate the disease severity.
The proposed method was an efficient tool for disease severity estimation in field conditions. This study can facilitate the implementation of artificial intelligence for rapid disease severity estimation and control in agriculture.
疾病严重程度的自动准确估计对于疾病管理和产量损失预测至关重要。传统的疾病严重程度估计是使用背景简单的图像进行的，这在实际应用中存在局限性。因此，迫切需要开发一种基于田间条件下拍摄的叶片图像来估计植物疾病严重程度的方法，这极具挑战性，因为阳光强度不断变化，且图像背景复杂。
本研究使用优化的神经网络开发了一种简单准确的基于图像的疾病严重程度估计方法。提出了一种混合注意力和迁移学习优化的语义分割模型来获取病害分割图。通过病斑像素与叶片像素的比例来计算严重程度。使用在自然条件下收集的黄瓜霜霉病和白粉病叶片对所提出的方法进行了验证。结果表明，具有空间注意力和通道注意力相互作用的混合注意力可以提取精细的病斑和叶片特征, 迁移学习可以进一步提高模型的分割精度。所提出的方法能够准确分割健康叶片和病斑（平均交并比 = 81.23%，频率加权交并比 = 91.89%）。此外，还准确估计了黄瓜叶片病害的严重程度（R = 0.9578，均方根误差 = 1.1385）。此外，将所提出的模型与六种不同的主干网络和四种语义分割模型进行了比较。结果表明，所提出的模型在复杂条件下优于比较模型，并且可以细化病斑分割并准确估计疾病严重程度。
所提出的方法是田间条件下疾病严重程度估计的有效工具。本研究有助于在农业中实施人工智能以进行快速的疾病严重程度估计和控制。
