Mapping sunflower yield as affected by Ridolfia segetum patches and elevation by applying evolutionary product unit neural networks to remote sensed data

Computers and Electronics in Agriculture

Volumn 60, Issue 2, 2008, Pages 122-132

  Gutierrez P A ^b   Lopez Granados F ^b   Pena Barragan J M ^b   Jurado Exposito M ^b   Gomez Casero M T ^b   Hervas Martinez C ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

Author keywords

Artificial neural networks; Evolutionary algorithms; Remote sensing; Sunflower; Yield prediction

Indexed keywords

CROPS; DATA STRUCTURES; EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS; PHOTOGRAMMETRY; PREDICTIVE CONTROL SYSTEMS; PROBLEM SOLVING; REGRESSION ANALYSIS;

CROP PARAMETERS; PRECISION FARMING; SUNFLOWERS; YIELD PREDICTION;

REMOTE SENSING;

AERIAL PHOTOGRAPH; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CROP YIELD; DATA SET; DICOTYLEDON; ELEVATION; MULTISPECTRAL IMAGE; PRECISION AGRICULTURE; REMOTE SENSING; STATISTICAL APPLICATION; VEGETATION MAPPING; WEED;

HELIANTHUS; RIDOLFIA SEGETUM;

EID: 38149120654     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2007.07.011     Document Type: Article

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