Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species

Alisaac, Elias; Behmann, Jan; Kuśka, Matheus Thomas; Dehne, H.-W.; Mahlein, Anne-Katrin

doi:10.1007/s10658-018-1505-9

Cited by 63 publications

(65 citation statements)

References 62 publications

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“…Huang et al (2007) demonstrated reliable measurement of severity using a 9-class ordinal scale for severity of yellow rust in wheat (R 2 = 0.91). Other studies have explored classification accuracy using ordinal groupings in classes of visually assessed specimens as the assumed gold standard (Bravo et al 2003;Alisaac et al 2018;Thomas et al 2018a;Alisaac et al 2019), including the use of confusion matrices. Regression analysis of visual estimates of diseased wheat spikes on a percentage scale and hyperspectral measurements also had demonstrable reliability (R 2 up to 0.828, Kobayashi et al 2016).…”

Section: Ground Truthing Accuracy and Measuring Disease Severity Witmentioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases and is prone to error. Good quality disease severity data should be accurate (close to the true value). Earliest quantification of disease severity was by visual estimates. Sensor-based image analysis including visible spectrum and hyperspectral and multispectral sensors are established technologies that promise to substitute, or complement visual ratings. Indeed, these technologies have measured disease severity accurately under controlled conditions but are yet to demonstrate their full potential for accurate measurement under field conditions. Sensor technology is advancing rapidly, and artificial intelligence may help overcome issues for automating severity measurement under hyper-variable field conditions. The adoption of appropriate scales, training, instruction and aids (standard area diagrams) has contributed to improved accuracy of visual estimates. The apogee of accuracy for visual estimation is likely being approached, and any remaining increases in accuracy are likely to be small. Due to automation and rapidity, sensor-based measurement offers potential advantages compared with visual estimates, but the latter will remain important for years to come. Mobile, automated sensor-based systems will become increasingly common in controlled conditions and, eventually, in the field for measuring plant disease severity for the purpose of research and decision making.

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Section: Ground Truthing Accuracy and Measuring Disease Severity Witmentioning

confidence: 99%

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

et al. 2020

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“…The illumination source was a 150-W halogen lamp light. Finally, Alisaac, Behmann, Kuska, Dehne, & Mahlein (2018) used two different cameras (Hyperspectral Camera ImSpector V10 and SWIR-camera) with wavelengths ranging from 400 to 1000 and 1000 to 2500 nm, respectively. The samples were illuminated with Analytical Spectral Devices PRO-Lamps and the spectral measurements of both cameras were controlled by a Spectral Cube Software.…”

Section: Equipmentmentioning

confidence: 99%

Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review

et al. 2020

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“…Subsequently, the close operation was performed on the results. Lastly, the ratio of FHB-infected areas to the entire spike area was calculated in accordance with the standard [38,39]. The methods for classifying the reaction of wheat to FHB can vary depending on the intended purpose.…”

Section: Fhb Detectionmentioning

confidence: 99%

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

et al. 2019

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Fusarium head blight (FHB) is a devastating disease of wheat worldwide. In addition to reducing the yield of the crop, the causal pathogens also produce mycotoxins that can contaminate the grain. The development of resistant wheat varieties is one of the best ways to reduce the impact of FHB. To develop such varieties, breeders must expose germplasm lines to the pathogen in the field and assess the disease reaction. Phenotyping breeding materials for resistance to FHB is time-consuming, labor-intensive, and expensive when using conventional protocols. To develop a reliable and cost-effective high throughput phenotyping system for assessing FHB in the field, we focused on developing a method for processing color images of wheat spikes to accurately detect diseased areas using deep learning and image processing techniques. Color images of wheat spikes at the milk stage were collected in a shadow condition and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model detected spikes very accurately in the images since the coefficient of determination for the number of spikes tallied by manual count and the model was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results for spike detection, a new color feature was applied to obtain the gray image of each spike and a modified region-growing algorithm was implemented to segment and detect the diseased areas of each spike. Results showed that the region growing algorithm performed better than the K-means and Otsu’s method in segmenting diseased areas. We demonstrated that deep learning techniques enable accurate detection of FHB in wheat based on color image analysis, and the proposed method can effectively detect spikes and diseased areas, which improves the efficiency of the FHB assessment in the field.

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Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species

Cited by 63 publications

References 62 publications

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy

Use of hyperspectral imaging as a tool for Fusarium and deoxynivalenol risk management in cereals: A review

Detection of Fusarium Head Blight in Wheat Using a Deep Neural Network and Color Imaging

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