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This paper proposes an integrated deep learning and statistical framework for associating gene-environment interactions with whole-network leaf vascular architecture, using EDTER for edge detection and SSCCA for variable selection.
This paper proposes a hybrid quantum-classical workflow for plant phenomics classification under small-data regimes, using supervised latent restructuring (PCA + LDA) to improve geometric separability before quantum kernel alignment. Experiments show improved separability but highlight compression trade-offs and the difficulty of achieving strong quantum performance.