Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale
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 <i>Fusarium</i> head blight (FHB) caused by <i>Fusarium graminearum</i> a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2281 |
| _version_ | 1828109478590939136 |
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| author | Anne-Katrin Mahlein Elias Alisaac Ali Al Masri Jan Behmann Heinz-Wilhelm Dehne Erich-Christian Oerke |
| author_facet | Anne-Katrin Mahlein Elias Alisaac Ali Al Masri Jan Behmann Heinz-Wilhelm Dehne Erich-Christian Oerke |
| author_sort | Anne-Katrin Mahlein |
| collection | DOAJ |
| description | 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 <i>Fusarium</i> head blight (FHB) caused by <i>Fusarium graminearum</i> and <i>Fusarium culmorum</i>. 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 <i>Fusarium</i>-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with <i>F. graminearum</i>. 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. |
| first_indexed | 2024-04-11T11:02:54Z |
| format | Article |
| id | doaj.art-6a1973219d234c89b1a7ba07af637e3f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:02:54Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6a1973219d234c89b1a7ba07af637e3f2022-12-22T04:28:27ZengMDPI AGSensors1424-82202019-05-011910228110.3390/s19102281s19102281Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet ScaleAnne-Katrin Mahlein0Elias Alisaac1Ali Al Masri2Jan Behmann3Heinz-Wilhelm Dehne4Erich-Christian Oerke5Institute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, GermanyInstitute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, GermanyInstitute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, GermanyInstitute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, GermanyInstitute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, GermanyInstitute of Crop Science and Resource Conservation (INRES), Plant Diseases and Plant Protection, Rheinische Friedrich-Wilhelms Universität Bonn, Nussallee 9, 53115 Bonn, GermanyOptical 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 <i>Fusarium</i> head blight (FHB) caused by <i>Fusarium graminearum</i> and <i>Fusarium culmorum</i>. 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 <i>Fusarium</i>-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with <i>F. graminearum</i>. 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.https://www.mdpi.com/1424-8220/19/10/2281wheat<i>Fusarium graminearum</i><i>Fusarium culmorum</i>thermographychlorophyll fluorescence imaginghyperspectral imagingsupport vector machinemulti-sensor data |
| spellingShingle | Anne-Katrin Mahlein Elias Alisaac Ali Al Masri Jan Behmann Heinz-Wilhelm Dehne Erich-Christian Oerke Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale Sensors wheat <i>Fusarium graminearum</i> <i>Fusarium culmorum</i> thermography chlorophyll fluorescence imaging hyperspectral imaging support vector machine multi-sensor data |
| title | Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale |
| title_full | Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale |
| title_fullStr | Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale |
| title_full_unstemmed | Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale |
| title_short | Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale |
| title_sort | comparison and combination of thermal fluorescence and hyperspectral imaging for monitoring i fusarium i head blight of wheat on spikelet scale |
| topic | wheat <i>Fusarium graminearum</i> <i>Fusarium culmorum</i> thermography chlorophyll fluorescence imaging hyperspectral imaging support vector machine multi-sensor data |
| url | https://www.mdpi.com/1424-8220/19/10/2281 |
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