YNNM4DH8

2307

7 janvier 2021 14:14

7 janvier 2021 14:14

Unmanned Aerial Vehicles (UAVs) offer excellent survey capabilities at low cost to provide farmers with information about the type and distribution of weeds in their fields. In this study, the problem of detecting the infestation of a typical weed (charlock mustard) in an alfalfa crop has been addressed using conventional digital cameras installed on a lightweight UAV to compare RGB-based indices with the widely used Normalized Difference Vegetation Index (NDVI) index. The simple (R−B)/(R+B) and (R−B)/(R+B+G) vegetation indices allowed one to easily discern the yellow weed from the green crop. Moreover, they avoided the potential confusion of weeds with soil observed for the NDVI index. The small overestimation detected in the weed identification when the RGB indices were used could be easily reduced by using them in conjunction with NDVI. The proposed methodology may be used in the generation of weed cover maps for alfalfa, which may then be translated into site-specific herbicide treatment maps.

None

• AB - Transversal
• FREDO lutte
• FREDO technologie et innovation
• GEO Espagne
• GEO Portugal
• detection

• RGB sensor
• precision agriculture
• remote sensing
• unmanned aerial vehicle
• weed

AgriEngineering

2

206-212

2020/6

24 juin 2020

10.3390/agriengineering2020012 Le DOI est une URL unique de référencement d'une publication. Il est donc plus fiable et permanent qu'une URL classique