Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.

Apple proliferation (AP) and pear decline (PD) are important diseases of fruit trees in Europe that are difficult to control. Timely uprooting of infected trees is a successful measure to limit the spread of the diseases and is therefore mandatory in some production areas. Large-scale monitoring strategies are required. AP and PD induce in infected trees an early leaf reddening in late summer or early autumn that is suitable to be monitored by remote sensing. Very high-resolution satellite images covering the region of reference orchards were analysed with machine learning methods to identify apple and pear orchards and AP- and PD-infected trees. High accuracy of apple and pear orchard identification was possible with values from 0.98 to 0.99. Symptomatic trees were identified in multispectral data of the WorldView satellites with a spatial resolution of 15 cm × 15 cm. Vegetation indices which combine information on the biomass and pigment proved to be suitable for detecting both AP and PD at tree level. Vitality maps and diagnostic maps are two targeted products out of the current study. While the diagnostic map reveals trees with phytoplasma-induced symptoms such as red leaves at a specific time point, the vitality map also causally records other vitality parameters of the trees in a time series.