Training

Training Command

phenonn train [m] [pft] [nr_features] [target] [batch_size] [options]

Arguments

• m: Block size for shuffling or “full” for no shuffling

• pft: Plant functional type (DB, EN, GR)

• nr_features: Number of features (6, 8, 9, 14)

• target: Target variable to predict

• batch_size: Batch size for training

Target Variables

The following target variables can be predicted:

• gcc: Raw Green Chromatic Coordinate

• rcc: Raw Red Chromatic Coordinate

• gcc_lowess: GCC with LOWESS smoothing

• rcc_lowess: RCC with LOWESS smoothing

• gcc_norm: Normalized GCC

• rcc_norm: Normalized RCC

• gcc_lowess_norm: Normalized GCC with LOWESS

• rcc_lowess_norm: Normalized RCC with LOWESS

Training Parameters

Key training parameters:

Parameter	Description	Default
--epochs	Maximum number of training epochs	150
--learning-rate	Initial learning rate	0.01
--hidden-size	LSTM hidden layer size	64
--dropout	Dropout rate for regularization	0.0
--patience	Early stopping patience	30

Cross-Validation

PhenoNN uses leave-one-site-out cross-validation: - Training data: All sites except one - Validation data: The held-out site - Models are trained for each fold, resulting in multiple models per PFT

Example

# Train a model for DB PFT with 8 features
phenonn train full DB 8 gcc_lowess 8 --epochs 100 --learning-rate 0.01