Assessing Wheat Yellow Rust Disease through Hyperspectral Remote Sensing

24 citations as recorded by crossref.

1. Classification of yellow rust of wheat from Sentinel-2 satellite imagery using deep learning artificial neural network H. Singh et al. https://doi.org/10.1007/s12517-023-11750-1
2. Advancing wheat crop analysis: A survey of deep learning approaches using hyperspectral imaging F. Zidi et al. https://doi.org/10.1016/j.compag.2025.110770
3. Early Onset Yellow Rust Detection Guided by Remote Sensing Indices V. Thirugnana Sambandham et al. https://doi.org/10.3390/agriculture12081206
4. Development of Methods for Remote Monitoring of Leaf Diseases in Wheat Agrocenoses I. Sereda et al. https://doi.org/10.3390/plants12183223
5. Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing G. Krishna et al. https://doi.org/10.1016/j.agwat.2018.08.029
6. Identification of Spectral Bands to Discriminate Wheat Spot Blotch using in Situ Hyperspectral Data N. Chattopadhyay et al. https://doi.org/10.1007/s12524-023-01673-5
7. Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning S. Appeltans et al. https://doi.org/10.3390/rs13071341
8. Evaluation of Stem Rust Disease in Wheat Fields by Drone Hyperspectral Imaging J. Abdulridha et al. https://doi.org/10.3390/s23084154
9. Non-invasive diagnosis of wheat stripe rust progression using hyperspectral reflectance J. Cross et al. https://doi.org/10.3389/fpls.2024.1429879
10. Wheat Yellow Rust Disease Infection Type Classification Using Texture Features U. Shafi et al. https://doi.org/10.3390/s22010146
11. Comparative evaluation of hybrid and individual models for predicting soybean yellow mosaic virus incidence Y. Khan et al. https://doi.org/10.1038/s41598-025-99427-5
12. Detection of Aphid-Infested Mustard Crop Using Ground Spectroscopy K. Shukla et al. https://doi.org/10.3390/rs16010047
13. Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 1: Laboratory study R. Whetton et al. https://doi.org/10.1016/j.biosystemseng.2017.11.008
14. Hyperspectral measurements of yellow rust and fusarium head blight in cereal crops: Part 2: On-line field measurement R. Whetton et al. https://doi.org/10.1016/j.biosystemseng.2018.01.004
15. Effects of feeding injury from Popillia japonica (Coleoptera: Scarabaeidae) on soybean spectral reflectance and yield A. Ribeiro et al. https://doi.org/10.3389/finsc.2022.1006092
16. Determination of the susceptible areas to wheat rust outbreak using threshold values and satellite images p. hasanteimouri & m. mobasheri https://doi.org/10.52547/jgst.12.2.47
17. FUSION OF DEEP LEARNING TECHNIQUES WITH TIME SERIES ANALYSIS FOR CROP YIELD PREDICTION FROM SATELLITE REMOTE SENSING DATA I. Dubey & D. Motwani https://doi.org/10.29121/shodhkosh.v5.i1.2024.3127
18. Integrating RGB Image Processing and Random Forest Algorithm to Estimate Stripe Rust Disease Severity in Wheat A. Wójtowicz et al. https://doi.org/10.3390/rs17172981
19. Detection of adulteration of non-transgenic soybean oil with transgenic soybean oil by integrating absorption, scattering with fluorescence spectroscopy X. He et al. https://doi.org/10.1007/s11694-024-03058-9
20. Next generation phenotyping for developing climate resilient rice varieties R. Sahoo et al. https://doi.org/10.35709/ory.2019.56.s.2
21. Early detection of stripe rust infection in wheat using light-induced fluorescence spectroscopy B. Atta et al. https://doi.org/10.1007/s43630-022-00303-2
22. Fusion of Feature Selection Methods and Regression Algorithms for Predicting the Canopy Water Content of Rice Based on Hyperspectral Data O. Elsherbiny et al. https://doi.org/10.3390/agriculture11010051
23. Spectral-temporal-spatial CapsNet (STS-CapsNet): A biology-informed network for continues detection of wheat canopy yellow rust dynamics from asymptomatic to mid-late stages using hyperspectral time-series J. Bi et al. https://doi.org/10.1016/j.atech.2025.101479
24. Next generation phenotyping for developing climate resilient rice varieties R. Sahoo et al. https://doi.org/10.35709/ory.2019.56.spl.2