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Early detection of tomato spotted wilt virus infection in tobacco using the hyperspectral imaging technique and machine learning algorithms

机译:利用高光谱成像技术和机器学习算法,早期发现番茄斑点枯萎病毒感染烟草

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The hyperspectral imaging technique was used for the non-destructive detection of tomato spotted wilt virus (TSWV) infection in tobacco at an early stage. Spectra ranging from 400 to 1000 nm with 128 bands from inoculated and healthy tobacco plants were analyzed by using three wavelength selection methods (successive projections algorithm (SPA), boosted regression tree (BRT), and genetic algorithm (GA)), and four machine learning (ML) techniques (boosted regression tree (BRT), support vector machine (SVM), random forest (RF), and classification and regression tress (CART)). The results indicated that the models built by the BRT algorithm using the wavelengths selected by SPA as the input variables obtained the best outcome for the 10-fold cross-validation with the mean overall accuracy of 85.2% and area under receiver operating curve (AUC) of 0.932. The band selection results and variable contribution analysis in BRT modeling jointly showed that the near-infrared (NIR) spectral region is informative and important for the differentiation of infected and healthy tobacco leaves. Different stages of post-inoculation were split according to the molecular identification and visual observation. The classification results at different stages indicated that the hyperspectral imaging data combined with ML methods and wavelength selection algorithms can be used for the early detection of TSWV in tobacco, both at the presymptomatic stage and during the period before the systematic infection can be detected by the molecular identification approach.
机译:高光谱成像技术用于在早期烟草中番茄斑枯萎病毒(TSWV)感染的非破坏性检测。通过使用三个波长选择方法(连续投影算法(SPA),提升回归树(BRT)和遗传算法(GA))和四台机器,分析了来自接种和健康烟草植物的128个带接种和健康烟草植物的128个带的光谱。和遗传算法(GA))和四台机器学习(ML)技术(提升回归树(BRT),支持向量机(SVM),随机林(RF)和分类和回归束(推车))。结果表明,BRT算法建立的模型使用SPA选择的波长作为输入变量获得了10倍交叉验证的最佳结果,其平均总精度为85.2%和接收器操作曲线(AUC)下的面积0.932。 BRT建模中的频带选择结果和可变贡献分析共同地表明,近红外(NIR)光谱区域是信息性的,对于感染和健康烟草叶的分化是重要的。根据分子鉴定和视觉观察分离接种后的不同阶段。在不同阶段的分类结果表明,与ML方法和波长选择算法相结合的高光谱成像数据可用于在烟草中早期检测假设阶段,并且在系统感染可以检测到系统感染之前分子识别方法。

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