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


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.


Computer Science

Second Department

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

INSPIRE - University Transportation Center

Second Research Center/Lab

Intelligent Systems Center


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.

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)


Document Type

Article - Journal

Document Version


File Type





© 2018 American Society for Photogrammetry and Remote Sensing, All rights reserved.

Publication Date

01 Jul 2018