Solanum tuberosum L.) growing seasons under varied nitrogen (N)-rates ranging from 0 to 639 kg/ha. The raw data were preprocessed using Pix4Dmapper and the quantum geographic information system. A linear unmixing model followed by Otsu-based adaptive autosegmentation was implemented to generate soil-masked spatio-spectral fusion maps for accurate vegetation feature extraction. The proposed feature engineering and prediction model followed a two-fold approach: first, adoption of partial least squares regression (PLSR) algorithm to extract features relevant to yield, and second, a novel context-aware attention and residual connection convolution-bidirectional gated recurrent unit bidirectional long short-term memory-network (CAR Conv1D-BiGRU-BiLSTM-Net) to exploit time-series multifeatures information to predict final yield. On integrating the PLSR-derived robust features, the proposed model demonstrated an increase in predictive capability from emergence (T1) to bulking (T4) growth stage by effectively capturing the temporal dynamics of physiological and biological traits. Overall, using multifeatures such as simple ratio, Chlorophyll Green, modified anthocyanin reflectance index, vegetation fraction ($V_{f}$), and N-rate from T1–T4 growth stage resulted in predictive accuracy with high $\text{R}^{2}$ = 0.775 and low root mean square error of 16.4%, outperforming other deep learning models.">

Context-Aware Deep Learning Model for Yield Prediction in Potato Using Time-Series UAS Multispectral Data (original) (raw)

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