Francisca López-Granados

TL;DR: In this article, a UAV equipped with a commercial camera (visible spectrum) was used for ultra-high resolution image acquisition over a wheat field in the early-season period, and six visible spectral indices (CIVE, ExG, ExGR, Woebbecke Index, NGRDI, VEG) and two combinations of these indices were calculated and evaluated for vegetation fraction mapping, to study the influence of flight altitude (30 and 60m) and days after sowing (DAS) from 35 to 75 DAS on the classification accuracy.

TL;DR: The results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).

TL;DR: A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain this article.

TL;DR: The current status of remote and proximal (on-ground) weed detection systems for site-specific weed management and the limitations and opportunities of these technologies are described.

TL;DR: A study of the accuracy of five supervised classification methods using multispectral and pan-sharpened QuickBird imagery to verify whether remote sensing offers the ability to efficiently identify crops and agro-environmental measures in a typical agricultural Mediterranean area characterized by dry conditions.