LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction
Fig 2
Overview of the proposed G × E prediction pipeline.
Genomic markers are reduced to 548 principal components and used as genotype node features. Daily weather variables are encoded via LSTM into a 21-dimensional environment embedding. These embeddings form nodes in a GNN, followed by an MLP predictor for yield estimation.