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Rakhesh Devadas

Researcher at University of Technology, Sydney

Publications -  19
Citations -  692

Rakhesh Devadas is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Wireless network & Rust. The author has an hindex of 12, co-authored 19 publications receiving 563 citations. Previous affiliations of Rakhesh Devadas include University of New England (United States) & University of New England (Australia).

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Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves

TL;DR: Ten, widely-used vegetation indices, based on mathematical combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate leaves of 1 month old wheat plants infected with yellow (stripe), leaf and stem rust.
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Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

TL;DR: In this paper, the authors focused on Australia, a continent with one of the most vari- able rainfall climates in the world and vast areas of dryland systems, where a detailed phenological investigation and a characterization of the relationship between phenology and climate variability are missing.
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A spatially explicit land surface phenology data product for science, monitoring and natural resources management applications

TL;DR: The product's algorithm and information content consisting of metrics that characterize LSP greening and browning episodes of the vegetated land surface allows tracking LSP metrics over time and thereby quantifying inter- and intraannual variability across Australia.
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Effect of stripe rust on the yield response of wheat to nitrogen

TL;DR: It was evident from the experiment that stripe rust caused yield loss accompanied by either no change or reduction in GPC, indicating that the total amount of N entering the grain was reduced by stripe rust, which is most likely associated with reduced uptake of N during grain filling.
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A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

TL;DR: The complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop.