A deep learning architecture for leaf water potential prediction in Populus euramericana ‘I-214’ from hyperspectral reflectance - Gong, Xue-Wei; Yu, Qing-Song; Li, Hong-Li; Fang, Zhuo-Qun; Guo, Jia-Xu; Li, Zhao-Kui; Wang, Heng-Fang; Pang, Zhong-Yi; Peng, Yan-Hui; Sun, Xue-Kai; Hao, Guang-You (Frontiers in Plant Science, 2026)
IntroductionLeaf water potential (Ψleaf) is a fundamental physiological metric quantifying tree water status and forest drought stress, yet its measurement remains labor-intensive and destructive. Hyperspectral techniques show great promise for retrieving plant physiological traits; however, robust Ψleaf estimation remains limited by three critical factors: unbalanced data distributions, the need for global–local feature synergy, and inherent uncertainty in point-based regression. MethodsHere, we propose a deep learning framework (CIDL) that integrates: (1) a conditional generative adversarial network (CGAN) to generate balanced synthetic samples across the full Ψleaf domain; (2) a feature extractor that combines Inception–ResNet with ACmix (IRAC) to capture local absorption features and long-range spectral dependencies jointly; and (3) a distribution-aware regression network (DARN) to explicitly model the target-variable distribution, thereby enhancing predictive reliability. The model was trained and evaluated using a dataset derived from dehydration experiments on leaves of young Populus euramericana ‘I-214’ trees, comprising 229 paired Ψleaf and hyperspectral reflectance measurements, which were further augmented with 500 CGAN-generated synthetic samples to improve model robustness. ResultsCIDL achieved a prediction accuracy of R2 = 0.78 and RMSE = 0.27 MPa on the test set, clearly outperforming traditional machine learning methods (mean R2 = 0.66, mean RMSE = 0.34 MPa) and yielding a modest yet consistent improvement over mainstream deep learning approaches (mean R2 = 0.76, mean RMSE = 0.28 MPa). DiscussionThese results demonstrate that the proposed CIDL framework provides a generalizable solution for small-sample physiological hyperspectral analysis and offers a reliable, non-destructive pathway for tree water-stress monitoring, with strong potential for applications in smart forestry management.