Tar spot disease is a fungal disease that appears as a series of black circular spots containing spores on corn leaves. Tar spot has proven to be an impactful disease in terms of reducing crop yield. To quantify disease progression, experts usually have to visually phenotype leaves from the plant. This process is very time-consuming and is difficult to incorporate in any high-throughput phenotyping system. Deep neural networks could provide quick, automated tar spot detection with sufficient ground truth. However, manually labeling tar spots in images to serve as ground truth is also tedious and time-consuming. In this paper we first describe an approach that uses automated image analysis tools to generate ground truth images that are then used for training a Mask R-CNN. We show that a Mask R-CNN can be used effectively to detect tar spots in close-up images of leaf surfaces. We additionally show that the Mask R-CNN can also be used for in-field images of whole leaves to capture the number of tar spots and area of the leaf infected by the disease.

翻译：谷类斑点疾病是一种真菌病,它作为一系列黑环斑,含有玉米叶上的螺旋质。Tar点在减少作物产量方面被证明是一种影响极大的疾病。为了量化疾病进展,专家通常需要从植物中提取视觉的苯型叶子。这一过程非常耗时,难以纳入任何高通量切口状系统。深神经网络可以提供快速、自动的焦油点检测,并有足够的地面真相。然而,在图像中人工标出焦油点,作为地面真相也是乏味和耗时的。本文首先描述了一种使用自动图像分析工具生成地面真实图像的方法,这些图像随后用于培训一个Mask R-CNN。我们表明,Mas R-CNN可以有效地用于探测叶表面近端图像中的焦油点。我们还表明,遮罩 R-CNN还可以用于整个叶叶的实地图像,以捕捉受该疾病感染的焦点和叶片面积。