4.6 Article

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

期刊

SENSORS
卷 19, 期 10, 页码 -

出版社

MDPI
DOI: 10.3390/s19102281

关键词

wheat; Fusarium graminearum; Fusarium culmorum; thermography; chlorophyll fluorescence imaging; hyperspectral imaging; support vector machine; multi-sensor data

资金

  1. German Federal Ministry of Education and Research (BMBF) [0315529]
  2. Catholic Academic Exchange Service (KAAD), Scholarship Program 2
  3. project PLANT FOOD SEC from the European Union (Seventh framework program) [261752]
  4. German Academic Exchange Service (DAAD)

向作者/读者索取更多资源

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.

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