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Q47162398-0CC7A862-E47D-4930-B1C6-D324C17E84D0
Q47162398-0CC7A862-E47D-4930-B1C6-D324C17E84D0
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http://www.wikidata.org/entity/statement/Q47162398-0CC7A862-E47D-4930-B1C6-D324C17E84D0
Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV).
P2860
Q47162398-0CC7A862-E47D-4930-B1C6-D324C17E84D0
BestRank
Statement
http://www.wikidata.org/entity/statement/Q47162398-0CC7A862-E47D-4930-B1C6-D324C17E84D0
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wasDerivedFrom
3993c5e1f5d7d09842c1bed37020745642f29e2d
P2860
PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.