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

ORCID

Yadav: https://orcid.org/0000-0002-8158-7207; Zhang: https://orcid.org/0000-0001-9654-3859; Wijewardane: https://orcid.org/0000-0001-8962-9451; Huang: https://orcid.org/0000-0002-1409-8868

MSU Affiliation

College of Agriculture and Life Sciences; Department of Agricultural and Biological Engineering; James Worth Bagley College of Engineering

Creation Date

2026-06-30

Abstract

The study demonstrated the efficacy of integrating time-series uncrewed aerial system (UAS) multispectral imaging with data-driven deep learning methodologies to systematically and precisely predict field-scale crop yield throughout the growing seasons. A UAS equipped with a micasense rededge MX+ sensor was used for data acquisition at the Hermiston Agricultural Research and Extension Center, Oregon State University. The data were collected throughout the potato (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 (Vf), and N-rate from T1–T4 growth stage resulted in predictive accuracy with high R2 = 0.775 and low root mean square error of 16.4%, outperforming other deep learning models.

Publication Date

2-5-2024

Publication Title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Publisher

Institute of Electrical and Electronics Engineers

First Page

6096

Last Page

6115

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Rights

© 2025 The Authors

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Digital Object Identifier (DOI)

https://doi.org/10.1109/JSTARS.2025.3539217