Early Drought Plant Stress Detection with Bi-Directional Long-Term Memory Networks
Alternative Title
Early Drought Stress Detection with Bidirectional Long Short-Term Memory Networks
Abstract
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.
Recommended Citation
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
Department(s)
Computer Science
Second Department
Civil, Architectural and Environmental Engineering
Research Center/Lab(s)
INSPIRE - University Transportation Center
Second Research Center/Lab
Intelligent Systems Center
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)
0099-1112
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 American Society for Photogrammetry and Remote Sensing, All rights reserved.
Publication Date
01 Jul 2018
Comments
This work is supported by NSF CAREER award IIS-1351049, NSF EPSCoR Grant IIA-1355406, Intelligent System Center and Center of Biomedical Science and Engineering at Missouri University of Science and Technology.