The fungal pathogen Rhizoctonia solani is one of the most important soil-borne diseases in sugar beet production worldwide. Root and crown rot caused by this fungus are traditionally recognized later in the cropping season by rating the above-ground symptoms like wilting and chlorosis on foliage, and dark brown lesions at the base of petioles. The present study was designed to evaluate noninvasive sensors and machine learning for measuring disease incidence and early detection. Eight-weeks-old plants were inoculated with the pathogen in two different concentrations and under controlled conditions. Hyperspectral images in the visible and near-infrared range from leaf were obtained in time-series. One hundred thirty and fifteen spectral features were selected in two levels by using the recursive feature elimination method (RFE) and a clustering approach. Subsequently, five types of machine-learning methods were employed to train four types of spectral data containing reflectance values, vegetation indices, selected variables of the RFE process and selected variables of an RFE-clustering process. The best classifier was obtained from a partial least squares modeling process and required a number of 15 spectral features, which include first and second derivatives of the wavelength spectrum as well as the Ctr3, EVI and PSSRa vegetation index. This investigation proves that under controlled conditions early detection of indirect symptoms caused by Rhizoctonia root rot in sugar-beet plants is possible. Scoring of disease incidence of Rhizoctonia root rot at 10 dai was 3 to 5 times higher with a machine-learning classifier in comparison with the human visual rating.

中文翻译：

---

甜菜根瘤菌诱导的甜菜症状的高光谱成像：输入数据和不同机器学习算法的比较

该真菌病原体纹枯病是全球甜菜生产中最重要的土壤传播疾病之一。传统上，这种真菌引起的根腐病是在作物季节后期通过对地上症状进行评分，例如叶子上的枯萎和萎黄病，以及在叶柄底部的深棕色病害。本研究旨在评估无创传感器和机器学习，以测量疾病发生率和早期检测。八周大的植物在控制条件下接种了两种不同浓度的病原体。在时间序列上获得了叶片可见和近红外范围内的高光谱图像。通过使用递归特征消除方法（RFE）和聚类方法，在两个级别中选择了135个光谱特征。后来，使用五种机器学习方法来训练四种类型的光谱数据，这些光谱数据包含反射率值，植被指数，RFE过程的选定变量和RFE聚类过程的选定变量。最佳分类器是通过偏最小二乘建模过程获得的，并且需要多个15个光谱特征，其中包括波长光谱的一阶和二阶导数以及Ctr3，EVI和PSSRa植被指数。这项研究证明，在受控条件下，可以早期发现甜菜植物中根瘤菌根腐病引起的间接症状。与人类的视觉等级相比，使用机器学习分类器在10 d时，根瘤菌根腐病的发病率得分高出3至5倍。