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.
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Document Type: Research Article
Publication date: July 1, 2018
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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