The aim of this study is to determine the severity of the disease Septoria leaf spot in tomato plants, through computational analysis of digital images of leaves affected. We collected and obtained digital images of tomato leaves with absence and presence of the disease with varying degrees of severity. From a script written in R with the EBImage package, the image was decomposed into three levels of color (RGB) and, through the process of thresholding the image segmentation, was performed separating sheet and injuries in relation to the background, determining the percentage of damaged area. Statistical properties were extracted from the original images and, from them and the severity quantified by software, was realized the process of correlation and regression analysis to indicate a template that determines the percentage of damaged area through the properties of the images. Subsequently, these models were tested, with a new image bank, from the RMSE error measures. The methodology described, was able to identify and quantify the damaged areas of the leaves with symptoms of diseases, extract the statistical properties of the images as allowed to predict mathematical models with acceptable potential and quality for indirect determination of the percentage of injured area through the properties of the images.

中文翻译：

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数字图像检测番茄斑枯病严重程度

本研究的目的是通过对受影响叶子的数字图像进行计算分析，确定番茄植株中斑枯病病害的严重程度。我们收集并获得了番茄叶片的数字图像，其中不存在和存在具有不同严重程度的疾病。从用 EBImage 包用 R 编写的脚本中，图像被分解为三个颜色级别 (RGB)，并通过对图像分割进行阈值处理，对与背景相关的纸张和伤害进行分离，确定百分比损坏的区域。从原始图像中提取统计属性，并从中提取软件量化的严重性，实现了相关性和回归分析的过程，以指示通过图像属性确定损坏区域百分比的模板。随后，根据 RMSE 误差度量，使用新的图像库对这些模型进行了测试。所描述的方法能够识别和量化具有疾病症状的叶子受损区域，提取图像的统计特性，以便预测具有可接受潜力和质量的数学模型，通过图像的属性。