Temporal alignment of UAV hyperspectral and phenotype data via autoencoders for predicting nitrogen and nicotine...

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_F), a one-dimensional convolutional autoencoder (AE_C), and a long short-term memory (LSTM) autoencoder (AE_L). Within this framework, the autoencoders project the original multi-temporal observations into compact latent representations and subsequently reconstruct them on a standardized temporal grid, thereby preserving salient spectral-phenotypic information while enforcing dimensional consistency across years. For comparative analysis, we additionally considered a straightforward time-step removal (TSR) procedure, which discards non-overlapping observation dates among different years and thus serves as a baseline temporal-harmonization strategy. On the temporally aligned sequences, two representative recurrent-neural architectures-long short-term memory (LSTM) networks and gated recurrent unit (GRU) networks-were trained to establish predictive models for cNg and cNt. Overall, models trained on inputs aligned by the autoencoder-based schemes exhibited markedly higher predictive skill than their TSR-based counterparts, with the convolutional and LSTM autoencoders consistently yielding greater improvements than the fully connected variant. Among all evaluated configurations, the combinations of AE_L with LSTM and with GRU delivered the best performance, attaining coefficients of determination of 0.73 for cNg and 0.56 for cNt on the independent test set. Taken together, these results indicate that the proposed autoencoder-based temporal-alignment framework can effectively distil informative features from multi-year UAV hyperspectral and phenotypic observations and constitutes a promising tool for the quantitative prediction of post-curing quality indicators in flue-cured tobacco production.