Wheat yield prediction using machine learning and advanced sensing techniques

Computers and Electronics in Agriculture

Volumn 121, Issue , 2016, Pages 57-65

  Pantazi, X E ^a   Moshou, D ^a   Alexandridis, T ^a   Whetton, R L ^b   Mouazen, A M ^b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b CRANFIELD UNIVERSITY   (United Kingdom)

Author keywords

Artificial neural networks; Data fusion; NDVI; Precision farming; Satellite imagery; Soil spectroscopy

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; CONFORMAL MAPPING; CROPS; DATA FUSION; FORECASTING; FORESTRY; LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS; SATELLITE IMAGERY; SATELLITES; SELF ORGANIZING MAPS; SOILS;

COMPLEX RELATIONSHIPS; CROP GROWTH AND YIELDS; NDVI; NORMALIZED DIFFERENCE VEGETATION INDEX; PRECISION FARMING; SELF-ORGANIZING MODEL; SOIL SPECTROSCOPIES; SUPERVISED SELF-ORGANIZING MAP;

SOIL SURVEYS;

CROP YIELD; CROPPING PRACTICE; MACHINE LEARNING; NDVI; SAMPLING; SATELLITE IMAGERY; SEASON; SOIL PROPERTY; WHEAT;

BEDFORDSHIRE; ENGLAND; UNITED KINGDOM;

TRITICUM AESTIVUM;

EID: 84950349731     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.11.018     Document Type: Article

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