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Hyperspectral - the answer to water stress? (Part 2)
The research presented in Part 2 aims to derive a two-band optimised vegetation index using hyperspectral data captured across the visible near-infrared (VNIR) region of the electromagnetic (EM) spectrum.
By Marc Dukes, Nitesh Poona & Kyle Loggenberg · 5 min read
Hyperspectral - the answer to water stress? (Part 1)
This study presents a novel hyperspectral-machine learning methodology for the non-destructive identification of water-stressed vines in a Shiraz vineyard.
By Kyle Loggenberg, Albert Strever & Nitesh Poona · 8 min read