Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

Guo, Wei; Yang, Yifeng; Zhao, Hu; Song, Rui; Dong, Ping; Jin, Qian; Baig, Muhammad Hasan Ali; Liu, Zelong; Yang, Zihan

doi:10.3390/app11199230

Cited by 4 publications

(2 citation statements)

References 12 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the studies have also concluded that the comparative performance of disease-specific or newly developed indices for model fitting is highly improved against conventional indices [45,89] that support different other studies [99]; based on these results, a precise FHB monitoring program can be developed. Moreover, the better disease estimation performance of models through disease-specific indices or bands has also been proved by previous findings [42,99]. Another study also estimated the canopy scale scab disease considering the optimal window size of texture features [96].…”

Section: Quantitative Models For Scab Diseasesupporting

confidence: 59%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

Section: Quantitative Models For Scab Diseasesupporting

confidence: 59%

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

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Based on these results, a precise FHB monitoring program can be developed. Moreover, the better disease estimation performance of models through disease specific indices have also been proved by previous findings [58,59].…”

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

confidence: 59%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Evaluation of spatial resolution on crop disease detection based on multiscale images and category variance ratio

Zhao

Yang

et al. 2023

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data

Cited by 4 publications

References 12 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

Evaluation of spatial resolution on crop disease detection based on multiscale images and category variance ratio

Contact Info

Product

Resources

About