Evaluation of hierarchical self-organising maps for weed mapping using UAS multispectral imagery

Computers and Electronics in Agriculture

Volumn 139, Issue , 2017, Pages 224-230

  Pantazi, X E ^a   Tamouridou, A A ^a   Alexandridis, T K ^a   Lagopodi, A L ^a   Kashefi, J ^b   Moshou, D ^a  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b USDA ARS European Biological Control Laboratory   (Greece)

Author keywords

eBee; Neural networks; Precision farming; Site specific weed management; Unmanned aircraft system

Indexed keywords

FIXED WINGS; INFRARED DEVICES; MAPPING; NEURAL NETWORKS; REMOTE SENSING;

EBEE; HIGH RESOLUTION IMAGE; IDENTIFICATION RATES; MULTI-SPECTRAL CAMERAS; MULTI-SPECTRAL IMAGERY; PRECISION FARMING; UNMANNED AIRCRAFT SYSTEM; WEED MANAGEMENT;

UNMANNED AERIAL VEHICLES (UAV);

ACCURACY ASSESSMENT; AIRBORNE SURVEY; ARTIFICIAL NEURAL NETWORK; DATA ACQUISITION; GRASS; HIERARCHICAL SYSTEM; IMAGE RESOLUTION; IMAGERY; MAP; MAPPING METHOD; PIXEL; PRECISION AGRICULTURE; REMOTE SENSING; SPECTRAL ANALYSIS; UNMANNED VEHICLE; WEED; WEED CONTROL;

AVENA STERILIS; SILYBUM MARIANUM;

EID: 85019750379     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2017.05.026     Document Type: Article

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