Keyphrases
Machine Learning
100%
Rice
100%
Top-N Recommendation
100%
Sensor-based
100%
Multi-source Data Fusion
100%
Active Canopy Sensor
100%
Nitrogen nutrition Index
100%
Nitrogen Diagnosis
100%
N Diagnosis
100%
Index Prediction
80%
N Rate
60%
Sensor Data
60%
Kappa Coefficient
60%
Recommendation Model
60%
Random Forest Regression
60%
Rice Management
60%
Crop Sensor
40%
Environmental Conditions
40%
Plot Experiment
40%
N Use Efficiency
40%
Farm Conditions
40%
Stepwise multiple Linear Regression
40%
N Status
40%
Plant N Uptake
40%
Model-based Diagnosis
40%
Regression Forest
40%
Management Strategy
20%
Model Validation
20%
Nitrogen N
20%
Food Security
20%
Crop Nitrogen
20%
Evaluation Model
20%
Condition-dependent
20%
Increased Yield
20%
Model Calibration
20%
Diagnostic Accuracy
20%
Sustainable Development
20%
Prediction Method
20%
Non-destructive Diagnosis
20%
Management Condition
20%
Calibration Assessment
20%
Aboveground Biomass
20%
Accurate Determination
20%
Reflectance Data
20%
Model Performance
20%
Simple Regression
20%
Diagnosis Strategy
20%
Economic Optimum N Rate
20%
Prediction Approach
20%
Northeast China
20%
Recommendation Strategy
20%
Precision N Management
20%
Rice Yield
20%
Security Development
20%
Negative Environmental Impact
20%
Prediction Strategy
20%
Status Diagnosis
20%
Crop N Status
20%
Topdressing
20%
N Surplus
20%
Partial Factor Productivity
20%
Transplanting Density
20%
Active Crop Sensor
20%
Regional Application
20%
On-farm Trials
20%
Calibration Validation
20%
Farmer Management
20%
Earth and Planetary Sciences
Machine Learning
100%
Multisensor Fusion
100%
Food Security
33%
Aboveground Biomass
33%
Reflectance
33%
Environmental Impact Assessment
33%
Factor Productivity
33%
China
33%
Management Strategy
33%
Agricultural and Biological Sciences
Nutrition Assessment
100%
Learning System
100%
Machine Learning
100%
Aboveground Biomass
20%
Sustainable Development
20%