Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture.
about
Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining ApproachesPortfolio optimization for seed selection in diverse weather scenarios.Effect of Co-segregating Markers on High-Density Genetic Maps and Prediction of Map Expansion Using Machine Learning Algorithms.Augmentation of crop productivity through interventions of omics technologies in India: challenges and opportunities
P2860
Determining the most important physiological and agronomic traits contributing to maize grain yield through machine learning algorithms: a new avenue in intelligent agriculture.
description
2014 nî lūn-bûn
@nan
2014 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Determining the most important ...... ue in intelligent agriculture.
@ast
Determining the most important ...... ue in intelligent agriculture.
@en
type
label
Determining the most important ...... ue in intelligent agriculture.
@ast
Determining the most important ...... ue in intelligent agriculture.
@en
prefLabel
Determining the most important ...... ue in intelligent agriculture.
@ast
Determining the most important ...... ue in intelligent agriculture.
@en
P2860
P50
P1433
P1476
Determining the most important ...... nue in intelligent agriculture
@en
P2093
Mansour Ebrahimi
Navid Shekoufa
P2860
P304
P356
10.1371/JOURNAL.PONE.0097288
P407
P577
2014-05-15T00:00:00Z