ArticlePlant methods2026
Temporal alignment of UAV hyperspectral and phenotype data via autoencoders for predicting nitrogen and nicotine content in cured tobacco leaves.
Article in Plant methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Post-curing nitrogen content (cNg) and nicotine content (cNt) constitute fundamental chemical attributes that govern the quality of flue-cured tobacco leaves. In this study, we investigate the use of unmanned aerial vehicle (UAV)-borne hyperspectral imagery in conjunction with advanced sequence-learning models to obtain canopy-level spectral information and to quantitatively predict cNg and cNt at the field scale. Nevertheless, because field experiments conducted in different growing seasons follow heterogeneous observation schedules, the resulting time series differ in both length and temporal sampling density, which poses a substantial obstacle to the direct deployment of recurrent neural-network models. To mitigate this limitation, we devised three autoencoder-based temporal-alignment schemes to reconcile the temporal dimension of multi-year spectral-phenotypic sequences, namely a fully connected autoencoder (AE
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