Keyphrases
Aboveground Biomass
16%
Active Canopy Sensor
100%
Agronomic Traits
16%
Concentration Determination
33%
Concentration Variability
16%
Critical N Concentration
16%
Critical Nitrogen
66%
Curve Construction
16%
Curve-based
16%
Destructive Sampling
16%
Determination Approach
33%
Diagnosis Strategy
100%
Ecological Benefits
16%
Economic Benefits
50%
Edge-based
33%
Efficiency Benefit
16%
Environmental Factors
16%
Environmental Management
16%
Evaluation Dataset
33%
Evaluation Experiment
16%
Farm Conditions
50%
Farmer Practices
16%
Fusion Learning
16%
Gene-environment Interaction
16%
Gradient Boosting
16%
Increased Yield
16%
Index Determination
16%
Index Difference
100%
Index Prediction
50%
Index-based
16%
Japonica Rice
16%
Machine Learning
16%
Machine Learning Based
100%
Machine Learning Modeling
16%
Machine Learning Models
33%
Machine Learning Techniques
16%
Management Strategy
33%
Management System
16%
Multi-source Data
16%
Multi-source Data Fusion
50%
N Concentration
33%
N Diagnosis
83%
N Dilution Curve
66%
N Management
33%
N Status
16%
N Surplus
16%
N Use Efficiency
33%
Nitrogen Diagnosis
100%
Nitrogen N
66%
Nitrogen nutrition Index
83%
Normalized Difference
100%
Normalized Difference Vegetation Index
100%
Northeast China
16%
On-farm Trials
16%
Oryza Sativa
16%
Plant Nitrogen Concentration
50%
Prediction Model
16%
Random Forest
16%
Random Forest Regression
16%
Recommendation Strategy
100%
Red Edge
100%
Rice
100%
Rice Management
33%
Rice Varieties
16%
Rice Yield
16%
Simple Regression Model
16%
Status Diagnosis
16%
Top-N Recommendation
50%
Two-machine
16%
Vegetation Index
16%
XGBoost Regression
16%
Agricultural and Biological Sciences
Aboveground Biomass
16%
Asian Rice
16%
Genotyping
16%
Normalized Difference Vegetation Index
100%
Nutrition Assessment
83%
Red Edge
100%
Vegetation Index
16%
Food Science
Asian Rice
20%
Nutrition Assessment
100%