Detecting of apple valsa canker (AVC), which is caused by Valsa mali Miyabe et Yamada, at early infection stage is beneficial to disease prevention and control for ensuring yield and quality of apples. Applying simple, economical and non-destructive rapid detection method for the early diagnosis of AVC is of great significance for precision management of orchards. In this research, near-infrared (NIR) spectroscopy (900−1700 nm) and Raman scattering (0−2000 cm⁻¹) combined with machine learning algorithms were employed to diagnose 3 infection degrees of AVC (healthy, disease 1, disease 2) based on optimal variables which were selected by chemometric methods. NIR and Raman spectroscopy were obtained using portable spectrometers in reflection mode, respectively. Firstly, adaptive iterative reweighting partial least squares (air-PLS) was utilized to remove fluorescence background in Raman spectra. Secondly, clustering analysis was developed using principal component analysis (PCA). After that, optimal variables were selected by x-loadings (XLs) of PCA and competitive adaptive reweighted sampling (CARS) algorithm, respectively. Four optimal wavelengths at 983, 1156, 1395, and 1457 nm from NIR spectra and five optimal wavenumbers at 175, 229, 326, 409, and 1523 cm⁻¹ from Raman spectra were identified by XLs. Six optimal wavelengths at 943, 949, 967, 1240, 1632, 1666 nm from NIR spectra and eight optimal wavelengths at 412, 1500, 1585, 1596, 1599, 1602, 1672 and 1709 cm⁻¹ from Raman spectra were selected according to CARS. Finally, AVC diagnosing models were developed using least square support vector machine (LS-SVM), and receiver operating characteristic (ROC) curves were applied to evaluate the performance of the classification models. Overall, the relatively good classification results on optimal variables (94.67% and 97.33% in NIR, 89.33% and 89.33% in Raman) were obtained using LS-SVM. This study confirmed that molecular vibrational spectroscopy techniques (NIR and Raman) is promising for early detecting AVC and providing a practical way for diagnosing diseases in large-scale orchards.

在感染的早期阶段检测由Valsa mali Miyabe et Yamada引起的苹果valsa溃疡病（AVC）有助于预防和控制疾病，以确保苹果的产量和质量。应用简单，经济，无损的快速检测方法对AVC进行早期诊断对果园的精确管理具有重要意义。在这项研究中，近红外（NIR）光谱（900-1700 nm）和拉曼散射（0-2000 cm ^-1）与机器学习算法相结合，基于通过化学计量学方法选择的最佳变量来诊断AVC的3种感染程度（健康，疾病1，疾病2）。使用便携式光谱仪以反射模式分别获得NIR和拉曼光谱。首先，利用自适应迭代重加权偏最小二乘（air-PLS）去除拉曼光谱中的荧光背景。其次，使用主成分分析（PCA）进行聚类分析。之后，分别通过PCA的x负荷（XL）和竞争性自适应重加权采样（CARS）算法选择最佳变量。来自NIR光谱的983、1156、1395和1457 nm处的四个最佳波长和175、229、326、409和1523 cm处的五个最佳波数来自拉曼光谱的^-1通过XLs鉴定。来自NIR光谱的943、949、967、1240、1632、1666 nm处的六个最佳波长和412、1500、1585、1596、1599、1602、1672和1709 cm ^-1处的八个最佳波长根据CARS从拉曼光谱中选择。最后，使用最小二乘支持向量机（LS-SVM）开发了AVC诊断模型，并应用了接收器工作特性（ROC）曲线来评估分类模型的性能。总体而言，使用LS-SVM获得了最佳变量的相对较好的分类结果（NIR为94.67％和97.33％，拉曼为89.33％和89.33％）。这项研究证实了分子振动光谱技术（NIR和拉曼技术）对于早期检测AVC很有希望，并为诊断大型果园中的疾病提供了一种实用的方法。