Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive.

fastylosa（Xf）是一种有害的植物致病细菌，能够感染全球500多种植物。成功的根除和控制有害病原体的策略需要大规模的监测技术来检测受感染的宿主，即使它们没有显示出视觉症状。尽管先前使用机载高光谱和热成像的研究显示出有希望的结果，可以及早发现受Xf感染的橄榄树（Olea europaea），但当采用这些技术对机载平台和卫星上的多光谱摄像机进行大规模监控时，还需要进一步的工作。 。我们使用了Xf两年空降战役中收集的高光谱和热成像意大利南部的传染病地区，以评估针对此任务的频谱受限机器学习算法的性能。该算法用于评估选自原始高光谱图像的多光谱波段集，该波段集与从无人平台和有人驾驶飞机进行的大规模监视兼容。此外，分别评估了从高光谱和热成像中检索到的太阳诱导叶绿素荧光（SIF）和基于温度的作物水分胁迫指数（CWSI）的贡献，以量化它们在检测Xf的算法中的相对重要性。感染。使用支持向量机算法的检测性能从使用包括SIF和CWSI的原始完整高光谱数据集时的〜80％（kappa，κ= 0.42）降低到当六个光谱带的最佳设置最敏感时的〜74％（κ= 0.36）到Xf中感染患者除了CWSI热指示器使用。当既不使用SIF也不使用CWSI时，检测结果的准确度不足70％（将κ降低至非常低的性能0.29），这表明对于Xf检测，树温比叶绿素荧光更重要。这项工作证明了大型Xf可以使用携带有限光谱带（例如，具有10 nm带宽的6到12个波段）的多光谱和热像仪的机载平台来支持监视，只要根据其对Xf症状的敏感性进行仔细选择即可。更准确地说，蓝色（对于从400到450 nm之间的波段可得出NPQI指数）和热（从树木的温度得出CWSI的波段）是它们对橄榄中Xf症状敏感的最关键的光谱区域。