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Open AccessArticle

Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale

1
Institute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, Germany
2
Institute of Sugar Beet Research (IfZ), Holtenser Landstraße 77, 37079 Göttingen, Germany
3
Spatial Business Integration GmbH, Marienburg 27, 64297 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(10), 2281; https://doi.org/10.3390/s19102281
Received: 24 April 2019 / Revised: 12 May 2019 / Accepted: 13 May 2019 / Published: 17 May 2019
(This article belongs to the Special Issue Advanced Sensor Technologies for Crop Phenotyping Application)
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. View Full-Text
Keywords: wheat; Fusarium graminearum; Fusarium culmorum; thermography; chlorophyll fluorescence imaging; hyperspectral imaging; support vector machine; multi-sensor data wheat; Fusarium graminearum; Fusarium culmorum; thermography; chlorophyll fluorescence imaging; hyperspectral imaging; support vector machine; multi-sensor data
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Mahlein, A.-K.; Alisaac, E.; Al Masri, A.; Behmann, J.; Dehne, H.-W.; Oerke, E.-C. Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale. Sensors 2019, 19, 2281.

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