SEAGAN applies a domain-specific graph attention network to classify limitation states in A-Ci curves, achieving F1-score 0.857 and accuracy 0.882 on synthetic data with known ground truth.
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MVOS_HSI delivers a consolidated Python library and command-line tool for end-to-end preprocessing of leaf-level hyperspectral imaging data in agricultural research.
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SEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes
SEAGAN applies a domain-specific graph attention network to classify limitation states in A-Ci curves, achieving F1-score 0.857 and accuracy 0.882 on synthetic data with known ground truth.
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MVOS_HSI: A Python Library for Preprocessing Agricultural Crop Hyperspectral Data
MVOS_HSI delivers a consolidated Python library and command-line tool for end-to-end preprocessing of leaf-level hyperspectral imaging data in agricultural research.