Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Ma, Huiqin; Huang, Wenjiang; Jing, Yuanshu; Pignatti, Stefano; Laneve, Giovanni; Dong, Yingying; Ye, Huichun; Liu, Linyi; Guo, Anting; Jiang, Jing

doi:10.3390/s20010020

Cited by 46 publications

(38 citation statements)

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“…The authors of [18] investigated the potential of hyperspectral data, when combined with leaf textures, to recognize yellow rust (Puccinia striiformis) infection on winter wheat and Fusarium spp. of wheat ears [19,20]. The Chinese team has discovered that the use of six optimal spectral bands, in the Visible and Near Infrared (VNIR) spectral regions, identified by applying a continuous wavelet analysis, and 24 texture features extracted from a principal component analysis (PCA) provides a very good sensitivity to the yellow rust disease [19].…”

Section: Introductionmentioning

confidence: 99%

“…of wheat ears [19,20]. The Chinese team has discovered that the use of six optimal spectral bands, in the Visible and Near Infrared (VNIR) spectral regions, identified by applying a continuous wavelet analysis, and 24 texture features extracted from a principal component analysis (PCA) provides a very good sensitivity to the yellow rust disease [19]. Moreover, to test their applicability to a hyperspectral data set, when available at the ESA or Chinese level, support vector machine (SVM) models were created.…”

Section: Introductionmentioning

confidence: 99%

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Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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“…Several scholars used nonimaging hyperspectral instruments for wheat FHB detection. Ma et al (2020) used a ground surface spectrometer to measure the spectra from the side angle of wheat ears and carried out wavelet transform combined with Fisher linear analysis to establish a wheat FHB identification model with an overall accuracy higher than 88%. Based on the spectra measured by Analytical Spectral Devices (ASD) spectrometer, Huang et al (2019a) extracted the derivative and absorption features and vegetation indices from the side angle of winter wheat ears, and then these features were used to construct effective identification models of disease severity under the combination of Fisher's linear discriminant analysis and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Identification of Fusarium head blight in wheat ears using vertical angle-based reflectance spectroscopy

et al. 2021

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Fusarium head blight (FHB) is a major disease that negatively affects wheat yield in China. Given that conventional reflectance spectroscopy measurements are perpendicular to crop canopy, the identification of FHB in wheat ears with the spectral data from the vertical angle can provide the possibility for large-scale monitoring. In this study, multi-features were selected and constructed to realize the identification of FHB in wheat ears from the vertical angle, and the influence of leafy and leafless samples were discussed. Firstly, the multi-features, such as band features, spectral position features, and vegetation indices for the leafy and leafless samples, were used to evaluate the ability to identify FHB, and correlation analysis was performed to select the effective features. In order to further reduce redundancy and enhance the separation capability of features, these candidate features were categorized into different feature sets based on Fisher score values. Then, the support vector machine (SVM) algorithm was used to construct the FHB identification model based on different feature sets of leafy and leafless samples. The optimal multi-features and the best classification accuracy were finally determined. The results were showed in the following: (1) The overall accuracies and Kappa coefficients of leafy samples could reach up to 65% and 0.28, respectively, whereas the values for the leafless samples could reach 81% and 0.63 in this model; (2) the optimal multi-features had great potential in identifying FHB-infected wheat ears; and (3) the presence of leaves would reduce the model's identification capability and adversely affected the identification of FHB in wheat ears. These results provide realistic theoretical references for large-scale FHB monitoring, which are conducive to the selective harvest of wheat.

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“…In recent years, deep learning method has been introduced into object segmentation and object counting [22]- [23]. Compared to conventional algorithms of machine learning, although the accuracy of deep learning is normally high, it needs many more samples and much more time for model training [24]- [29].…”

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confidence: 99%

Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging

et al. 2020

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Fusarium head blight (FHB) is one of the most important diseases in wheat worldwide. Evaluation and identification of effective fungicides are essential for control of FHB. However, traditional methods based on the manual disease severity assessment to evaluate the efficacy of fungicides are timeconsuming and laborsome. In this study, we developed a new method to rapidly assess the severity of FHB and evaluate the efficacy of fungicide application programs. Enhanced red-green-green (RGG) images were processed from acquired raw red-green-blue (RGB) images of wheat ear samples; the images were transformed in color spaces through K-means clustering for rough segmentation of wheat ears; a random forest classifier was used with features of color, texture, geometry and vegetation index for fine segmentation of disease spots in wheat ears; a newly proposed width mutation counting algorithm was used to count wheat ears; and the disease severity of the wheat ears groups was graded and the efficacy of six fungicides was evaluated. The results show that the segmentation algorithm could segment wheat ears from a complex field background. And the counting algorithm could effectively solve the problem of wheat ear adhesion and occlusion. The average counting accuracy of all and diseased wheat ears were 93.00% and 92.64%, respectively, with the coefficients of determination (R 2) of 0.90 and 0.98, and the root mean square error (RMSE) of 10.56 and 7.52, respectively. The new method could accurately assess the diseased levels of wheat eat groups infected by FHB and determine the efficacy of the six fungicides evaluated. The results demonstrate a potential of using digital imaging technology to evaluate and identify effective fungicides for control of the FHB disease in wheat and other crop diseases. INDEX TERMS Fusarium head blight, K-means clustering, random forest, width mutation counting algorithm, pesticide spraying.

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Identification of Fusarium Head Blight in Winter Wheat Ears Using Continuous Wavelet Analysis

Cited by 46 publications

References 46 publications

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Identification of Fusarium head blight in wheat ears using vertical angle-based reflectance spectroscopy

Evaluation of Efficacy of Fungicides for Control of Wheat Fusarium Head Blight Based on Digital Imaging

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