Tea leaf blight (TLB) is a common tea disease seriously affecting the quality and yield of tea. An accurate estimation of TLB severity can be used to guide tea farmers to reasonably spray pesticides. This study proposes an estimation method for TLB severity in natural scene images and consists of four main steps: segmentation of the diseased leaves, area fitting of the diseased leaves, segmentation of the disease spots, and estimation of disease severity. Target leaves with TLB in the tea images are segmented by combining the U-Net network and fully connected conditional random field to reduce the influence of complex background. An ellipse restoration method is proposed to generate an elliptic mask to fit the full size of the occluded or damaged TLB leaves. The disease spot regions are segmented from the TLB leaves by a support vector machine classifier to calculate the Initial Disease Severity (IDS) index. The IDS index, color features, and texture features of the TLB leaves are inputted into the metric learning model to finally estimated disease severity. Experimental results show that the proposed method has higher estimation accuracy and stronger robustness against occluded and damaged TLB leaves compared with conventional convolution neural network methods and classical machine learning techniques.

枯萎病（TLB）是一种常见的茶病，严重影响茶的质量和产量。准确估算TLB严重程度可用于指导茶农合理喷洒​​农药。这项研究提出了一种自然场景图像中TLB严重性的估计方法，包括四个主要步骤：患病叶子的分割，患病叶子的面积拟合，病斑的分割以及疾病严重性的估计。通过结合U-Net网络和完全连接的条件随机场对茶图像中具有TLB的目标叶片进行分割，以减少复杂背景的影响。提出了一种椭圆复原方法来生成椭圆形遮罩，以适合被遮挡或损坏的TLB叶子的整个大小。通过支持向量机分类器从TLB叶中分割出病斑区域，以计算出初始病害严重程度（IDS）指数。将TLB叶的IDS索引，颜色特征和纹理特征输入到度量学习模型中，以最终估计疾病的严重程度。实验结果表明，与传统的卷积神经网络方法和经典的机器学习技术相比，该方法具有更高的估计精度和更强的鲁棒性。