Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale
| Autor: | Ali Al Masri, Elias Alisaac, Jan Behmann, Heinz-Wilhelm Dehne, Anne-Katrin Mahlein, Erich-Christian Oerke |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
Fusarium 010504 meteorology & atmospheric sciences hyperspectral imaging lcsh:Chemical technology 01 natural sciences Biochemistry Analytical Chemistry Fusarium culmorum Fusarium graminearum chlorophyll fluorescence imaging multi-sensor data support vector machine thermography wheat Head blight lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Chlorophyll fluorescence 0105 earth and related environmental sciences biology Hyperspectral imaging biology.organism_classification Fluorescence Atomic and Molecular Physics and Optics Plant disease Horticulture Thermography 010606 plant biology & botany |
| Zdroj: | Sensors, Vol 19, Iss 10, p 2281 (2019) Sensors Volume 19 Issue 10 |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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