Globally, olive (Olea europaea L.) productivity is threatened by plant pathogens, particularly the fungus Verticillium dahliae (Vd) and the bacterium Xylella fastidiosa (Xf). Infections by these pathogens restrict water and nutrient flow through xylem, producing a similar set of symptoms that can also be confounded with water stress. Conventional in situ monitoring techniques are time consuming and expensive, necessitating the development of large-scale detection methods. Airborne hyperspectral and thermal imagery have been successfully used to detect both Xf and Vd infection symptoms independently, i.e., when only one of the two diseases is present. Nevertheless, the discrimination of Vd from Xf infections in contexts where both pathogens are present has not been addressed to date. This study proposes a three-stage machine learning algorithm to distinguish Vd infections from Xf infections, using a series of datasets from 27 olive orchards affected by Xf and Vd outbreaks in Italy and Spain between 2011 and 2017. Plant traits were derived from airborne hyperspectral and thermal imagery, including physiological indices from radiative transfer model inversion, Solar-induced Fluorescence emission (SIF_@760), the Crop Water Stress Index (CWSI), and a selection of narrow–band hyperspectral indices. Several distinct spectral traits successfully discriminated Xf from Vd infections. The three-stage method generated a false-positive rate of 9%, an overall accuracy (OA) of 98%, and a kappa coefficient (κ) of 0.7 when identifying Vd infections using a mixed Vd + Xf dataset. When identifying Xf infections, the false-positive rate was 4%, the OA was 92%, and κ was 0.8. These results indicate that hyperspectral and thermal traits can be used to discriminate Xf from Vd infection caused by the two xylem–limited pathogens that trigger similar visual symptoms.

在全球范围内，橄榄 ( Olea europaea L.) 的生产力受到植物病原体的威胁，尤其是真菌大丽轮枝菌( Vd ) 和细小木霉( Xf )。这些病原体的感染会限制水分和营养物质通过木质部的流动，从而产生一系列类似的症状，这些症状也可能与水分胁迫混淆。传统的原位监测技术耗时且昂贵，需要开发大规模检测方法。机载高光谱和热成像已成功用于检测Xf和Vd独立的感染症状，即当仅存在两种疾病中的一种时。然而，迄今为止尚未解决在两种病原体都存在的情况下区分Vd和Xf感染的问题。本研究提出了一种三阶段机器学习算法来区分Vd感染和Xf感染，使用来自2011 年至 2017 年意大利和西班牙受Xf和Vd爆发影响的 27 个橄榄园的一系列数据集。植物性状来源于空气中的高光谱和热成像，包括来自辐射传递模型反演的生理指标、太阳诱导的荧光发射 (SIF _@760)、作物水分胁迫指数 (CWSI) 和一系列窄带高光谱指数。几个不同的光谱特征成功地将Xf与Vd感染区分开来。在使用混合Vd  +  Xf数据集识别Vd感染时，三阶段方法产生了 9% 的假阳性率、98% 的总体准确度 (OA) 和 0.7 的 kappa 系数 (κ) 。在识别Xf感染时，假阳性率为 4%，OA 为 92%，κ 为 0.8。这些结果表明高光谱和热特性可用于区分Xf和Vd 由引发类似视觉症状的两种木质部限制性病原体引起的感染。