Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat

Shi, Yue; Huang, Wenjiang; González‐Moreno, Pablo; Luke, Belinda; Dong, Yingying; Zheng, Qiong; Ma, Huiqin; Liu, Linyi

doi:10.3390/rs10040525

Cited by 49 publications

(43 citation statements)

References 49 publications

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“…Remote sensing has become a feasible technology for disease detection and assessment over the last several decades. Diseases that have been detected using remote sensing include rust infection [6][7][8], Fusarium head blight [9,10], and powdery mildew [9][10][11][12] in wheat, bacterial leaf blight in rice [13,14], grey leaf spot in maize [15], and late blight disease and bacterial spot in tomato [16,17]. When plants are infected with diseases, the leaf water, pigment content and internal structure undergo changes, and these biochemical and biophysical changes are also reflected in the spectral characteristics of plants [18].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

Huang

et al. 2020

Remote Sensing

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Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

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Section: Introductionmentioning

confidence: 99%

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

Huang

et al. 2020

Remote Sensing

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“…The feasibility of CWA for the hyperspectral-data-based identification and detection of crop pests and diseases has been demonstrated [14][15][16][17]20]. Continuous wavelet transform (CWT) [39] is a wavelet analysis method for localizing the signal simultaneously in the time-frequency domain to detect and analyze weak signals at various scales and resolutions and to analyze multidimensional hyperspectral signals across a scale continuum [14,16,40]. Based on CWT, the original reflectance spectrum of each Fusarium-head-blight-infected ear is first converted to a wavelet coefficient spectrum set on multiple scales with a mother wavelet function, in which each scale corresponds to a frequency of spectral change: high scale corresponds to low frequency and low scale corresponds to high frequency.…”

Section: Wavelet Features Extraction For Fusarium Head Blight Using Cwamentioning

confidence: 99%

“…For instance, Zhang et al [14] accurately estimated the disease severity of powdery mildew on leaf level through the combination of CWA and partial least square regression. Zhang et al [15] and Shi et al [16] revealed the promising potential of CWA for the identification of wheat yellow rust. By using CWA, Luo et al [17] quantified wheat aphid infestation successfully.…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Jing

et al. 2019

Sensors

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Fusarium head blight in winter wheat ears produces the highly toxic mycotoxin deoxynivalenol (DON), which is a serious problem affecting human and animal health. Disease identification directly on ears is important for selective harvesting. This study aimed to investigate the spectroscopic identification of Fusarium head blight by applying continuous wavelet analysis (CWA) to the reflectance spectra (350 to 2500 nm) of wheat ears. First, continuous wavelet transform was used on each of the reflectance spectra and a wavelet power scalogram as a function of wavelength location and the scale of decomposition was generated. The coefficient of determination R2 between wavelet powers and the disease infestation ratio were calculated by using linear regression. The intersections of the top 5% regions ranking in descending order based on the R2 values and the statistically significant (p-value of t-test < 0.001) wavelet regions were retained as the sensitive wavelet feature regions. The wavelet powers with the highest R2 values of each sensitive region were retained as the initial wavelet features. A threshold was set for selecting the optimal wavelet features based on the coefficient of correlation R obtained via the correlation analysis among the initial wavelet features. The results identified six wavelet features which include (471 nm, scale 4), (696 nm, scale 1), (841 nm, scale 4), (963 nm, scale 3), (1069 nm, scale 3), and (2272 nm, scale 4). A model for identifying Fusarium head blight based on the six wavelet features was then established using Fisher linear discriminant analysis. The model performed well, providing an overall accuracy of 88.7% and a kappa coefficient of 0.775, suggesting that the spectral features obtained using CWA can potentially reflect the infestation of Fusarium head blight in winter wheat ears.

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“…Continuous wavelet analysis (CWA) is an effective noise reduction method and it can also enhance the details of spectral features of hyperspectral data [39][40][41]. Hence, CWA has been successfully utilized in quantitative remote sensing for retrieving functional traits of plants (e.g., leaf mass per area [42], canopy water content [43], leaf dry matter content and specific leaf area [44]).…”

Section: Introductionmentioning

confidence: 99%

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Wang

Xia

et al. 2019

Remote Sensing

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Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

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Wavelet-Based Rust Spectral Feature Set (WRSFs): A Novel Spectral Feature Set Based on Continuous Wavelet Transformation for Tracking Progressive Host–Pathogen Interaction of Yellow Rust on Wheat

Cited by 49 publications

References 49 publications

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

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

Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

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