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

Huang, Liusheng; Zhang, Hansu; Huang, Wenjiang; Dong, Yingying; Ma, Huiqin; Zhao, Jing

doi:10.1007/s12517-020-06350-2

Cited by 7 publications

(7 citation statements)

References 38 publications

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“…Therefore, scab attracts attention because of its wid tribution and severe impact on grain quality. However, the bibliometric revie yses of scab under ARS demand the significant attention of the scientific com hough the recent focus has been devoted to scab [40,[42][43][44][45] and other plan conclusive methodology at a large scale is still highly needed. * LLR abbreviation of log-likelihood ratio used to achieve the optimal results with maximum coverage and uniqueness.…”

Section: Description Of Cluster Analysismentioning

confidence: 99%

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

et al. 2023

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The study provides a comprehensive bibliometric analysis of imaging and non-imaging spectroscopy for wheat scab (INISWS) using CiteSpace. Therefore, we underpinned the developments of global INISWS detection at kernel, spike, and canopy scales, considering sensors, sensitive wavelengths, and algorithmic approaches. The study retrieved original articles from the Web of Science core collection (WOSCC) using a combination of advanced keyword searches related to INISWS. Afterward, visualization networks of author co-authorship, institution co-authorship, and country co-authorship were created to categorize the productive authors, countries, and institutions. Furthermore, the most significant authors and the core journals were identified by visualizing the journal co-citation, top research articles, document co-citation, and author co-citation networks. The investigation examined the major contributions of INISWS research at the micro, meso, and macro levels and highlighted the degree of collaboration between them and INISWS knowledge sources. Furthermore, it identifies the main research areas of INISWS and the current state of knowledge and provides future research directions. Moreover, an examination of grants and cooperating countries shows that the policy support from the People’s Republic of China, the United States of America, Germany, and Italy significantly benefits the progress of INISWS research. The co-occurrence analysis of keywords was carried out to highlight the new research frontiers and current hotspots. Lastly, the findings of kernel, spike, and canopy scales are presented regarding the best algorithmic, sensitive feature, and instrument techniques.

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Section: Description Of Cluster Analysismentioning

confidence: 99%

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

et al. 2023

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“…Canopy reflectance can be used to identify specific stressors and diseases by recording canopy reflectance at multiple time points over an extended period. To date, the reflectance-based examination of FHB infected wheat canopy has not been practiced while a number of studies have examined the field wheat targeting the spikes only, employing diverse types of sensors [8,49].…”

Section: Estimation Of Disease Severity Using Conventional and Newly ...mentioning

confidence: 99%

“…Meanwhile, 846 nm characterizes the structural variation in the nearinfrared region of canopy reflectance. However, the role of water can also be speculated upon, as blighted spikes typically contain less water than healthy spikes [8,9,49,53]. The authors of [25] identified a similar waveband 866 nm for rice leaf blast.…”

Section: Interpretation Of Selected Spectral Bands and Vegetation Ind...mentioning

confidence: 99%

“…Due to subtle changes in reflectance, visual and conventional spectral analysis for pre-symptomatic disease detection is difficult [11,19]. Various MLC approaches have been used to classify diseases in numerous studies [11,16,25,49,59]. There are different datasets used in this study, each with its own number of samples that could pose a challenge to CA, including the datasets of WFCI 1 and WFCI 2 .…”

Section: Disease Classification With Different Machine Learning Class...mentioning

confidence: 99%

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Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

et al. 2022

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Hyperspectral reflectance (HR) technology as proxy approach to diagnose fusarium head blight (FHB) in wheat crop could be a real-time and non-invasive approach for its in-field management to reduce grain damage. In-field canopy’s non-imaging HR (400–2400 nm using ground-based spectrometer system), photosynthesis rate (Pn) and disease severity (DS) data were simultaneously acquired from artificially inoculated wheat plots over a period of two years (2020 and 2021) in the field. Subsequently, continuous wavelet transform (CWT) was employed to select the consistent spectral bands (CSBs) and to develop the canopy-based difference indices with criterion of variable importance score using random forest—recursive feature elimination. Thereby, different machine learning algorithms were employed for FHB classification and multivariate estimation, and linear regression models to evaluate the newly developed indices against conventional vegetation indices. The results showed that inoculation reduced the Pn rate of spikes, elevated reflectance in visible and short-wave infrared regions and decreased in near infrared region at different days after inoculation (DAI). CWT analysis selected five CSBs (401, 460, 570, 786 and 840 nm) employing datasets from 2020 and 2021. These spectral bands were employed to develop wheat fusarium canopy indices (WFCI1 and WFCI2). Considering the average classification accuracy (ACA) in both years of experiments, WFCI1 manifested a maximum ACA of 75% at 5 DAI with DS of 9.73% which raised to 100% at 10 DAI with a DS of 18%. ACA mentions the averaged results of all machine learning classifiers (MLC). While in the perspective of MLC, random forest (RF) outperformed the rest of the MLC, individually, it revealed 100% classification accuracy through WFCI1 at DS 10.78% on the eight DAI. The univariate estimation of disease based on WFCI1 and WFCI2 with independent data produced R2 and root mean square error (RMSE) values of 0.80 and 14.7, and 0.81 and13.50, respectively. However, Knn regression analysis with both canopy indices (WFCI1 and WFCI2) manifested the maximum accuracy for disease estimation with an RMSE of 11.61 and R2 = 0.83. Conclusively, the newly proposed HR indices show great potential as proxy approach for detecting FHB at early stage and understanding the physical state of crops in field conditions for the better management and control of plant diseases.

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“…Conventional detection methods, such as sampling analysis, are mostly performed by experts and experienced farmers in the field. This approach cannot meet the requirements for monitoring large areas, and it is time-consuming and laborious [5,6]. Hyperspectral remote sensing technology can quickly monitor crop diseases and is suitable for largescale applications [7,8].…”

Section: Introductionmentioning

confidence: 99%

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Huang

et al. 2021

Agriculture

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Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

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

Cited by 7 publications

References 38 publications

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

Global Trends and Future Directions in Agricultural Remote Sensing for Wheat Scab Detection: Insights from a Bibliometric Analysis

Hyperspectral Reflectance Proxies to Diagnose In-Field Fusarium Head Blight in Wheat with Machine Learning

Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

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