Early Drought Plant Stress Detection with Bi-Directional Long-Term Memory Networks
Early drought stress detection is a promising strategy that enables us to move from a reactive to a more proactive approach to manage drought risks and impacts. In this work, we apply for the first time the Bidirectional Long Short-Term Memory (BLSTM) networks to RGB images for accurate drought plant stress detection in the early stage. In addition, an optimal data collection strategy (ODCS) is investigated to use less time and manpower for the purpose of accurate early drought stress condition detection. The proposed method is validated on two independently collected RGB image datasets. In both datasets, the BLSTM method achieves competitive classification performances compared to three other deep learning methods. By using the proposed ODCS, our method can use only 2/3 of the entire dataset to achieve 74.6 percent F-score for the patch sequence classification and 72.0 percent F-score for the image sequence classification.
H. Li et al., "Early Drought Plant Stress Detection with Bi-Directional Long-Term Memory Networks," Photogrammetric Engineering and Remote Sensing, vol. 84, no. 7, pp. 459-468, American Society for Photogrammetry and Remote Sensing, Jul 2018.
The definitive version is available at https://doi.org/10.14358/PERS.84.7.459
Civil, Architectural and Environmental Engineering
Keywords and Phrases
Deep learning; Drought; Stresses; Bi-directional; Classification performance; Drought stress; Drought stress conditions; Learning methods; Long term memory; Pro-active approach; Sequence classification; Classification (of information); Data acquisition; Data set; Detection method; Image analysis; Learning; Memory; Model validation
International Standard Serial Number (ISSN)
Article - Journal
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