Fusarium Wilt Detection in Phalaenopsis Through Integrated Hyperspectral Imaging and Deep Learning Techniques

Fusarium wilt is a threatening plant infection for Phalaenopsis plants. The disease presents with symptoms such as yellowing and wilting of leaves, leading to death and possible spread to neighboring healthy plants. This study explores the use of hyperspectral imaging techniques and deep learning mo...

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Bibliographic Details
Published inIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 9446 - 9449
Main Authors Chen, Shao-Ting, Ouyang, Yen-Chieh, Shih, Min-Shao, Liu, Tsang-Sen, Chang, Chein-I
Format Conference Proceeding
LanguageEnglish
Published IEEE 07.07.2024
Subjects
Accuracy
Automated Detection System
Data models
Deep learning
Fusarium Wilt on Phalaenopsis
Fuses
Geoscience and remote sensing
Hyperspectral Imaging
Manuals
Reliability
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Summary:Fusarium wilt is a threatening plant infection for Phalaenopsis plants. The disease presents with symptoms such as yellowing and wilting of leaves, leading to death and possible spread to neighboring healthy plants. This study explores the use of hyperspectral imaging techniques and deep learning models to develop a non-destructive and efficient method for fusarium wilt detection. To exploit potential correlations and patterns within spectral bands, we use a 2D-CNN model as the model backbone. Finally, the integration of hyperspectral image data collection and detection models enables automated and simplified execution, providing a practical system for detecting and managing wilt in Phalaenopsis plants without manual intervention. This integration enables efficient processing of collected hyperspectral imagery, feeding it into detection models and producing reliable results.
ISSN:2153-7003
DOI:10.1109/IGARSS53475.2024.10641033