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| 001 | 852606 | ||
| 005 | 20210129235144.0 | ||
| 024 | 7 | _ | |a 10.1186/s13007-018-0313-8 |2 doi |
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| 100 | 1 | _ | |a Thomas, Stefan |0 P:(DE-Juel1)162287 |b 0 |
| 245 | _ | _ | |a Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform |
| 260 | _ | _ | |a London |c 2018 |b BioMed Central |
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| 520 | _ | _ | |a BackgroundPhenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size.ResultsSix barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well.ConclusionThe hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding. |
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| 700 | 1 | _ | |a Behmann, Jan |0 P:(DE-HGF)0 |b 1 |e Corresponding author |
| 700 | 1 | _ | |a Steier, Angelina |0 P:(DE-Juel1)161457 |b 2 |
| 700 | 1 | _ | |a Kraska, Thorsten |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Muller, Onno |0 P:(DE-Juel1)161185 |b 4 |
| 700 | 1 | _ | |a Rascher, Uwe |0 P:(DE-Juel1)129388 |b 5 |
| 700 | 1 | _ | |a Mahlein, Anne-Katrin |0 P:(DE-HGF)0 |b 6 |
| 773 | _ | _ | |a 10.1186/s13007-018-0313-8 |g Vol. 14, no. 1, p. 45 |0 PERI:(DE-600)2203723-8 |n 1 |p 45 |t Plant methods |v 14 |y 2018 |x 1746-4811 |
| 856 | 4 | _ | |y OpenAccess |u https://juser.fz-juelich.de/record/852606/files/s13007-018-0313-8.pdf |
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