Semantic segmentation with rule-based multitask learning for precise rice lodging identification

Hsin-Hung Tseng; Ming-Der Yang

doi:10.1117/12.3046019

20 December 2024 Semantic segmentation with rule-based multitask learning for precise rice lodging identification

Hsin-Hung Tseng, Ming-Der Yang

Author Affiliations +

Proceedings Volume 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII; 132660E (2024) https://doi.org/10.1117/12.3046019
Event: Asia-Pacific Remote Sensing, 2024, Kaohsiung, Taiwan

Abstract

Efficient disaster surveys can safeguard the compensation rights of affected farmers and serve as a critical component of market stabilization strategies. Due to climate change, Taiwan is experiencing more frequent climate disasters. The agricultural industry faces severe labor shortages making post-disaster recovery increasingly challenging. This issue may impact food supply security, disrupt prices, and threaten national security. To address this, the study applies an advanced and efficient semantic segmentation network model to the Unmanned Aerial Vehicle (UAV) captured imagery for rice lodging disaster assessment. By incorporating a rule-based multi-task learning framework, prior knowledge from physical rules constrains the classifier's learning. Preliminary results indicate that the modified model achieved a 10% above improvement in the recall rate for lodged rice compared to the original model using 2017 data, and around a 5% improvement on the transferred 2019 data. Suggesting that this study can predict rice lodging with a more interpretable model architecture and achieve better classification results.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Hsin-Hung Tseng and Ming-Der Yang "Semantic segmentation with rule-based multitask learning for precise rice lodging identification", Proc. SPIE 13266, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications VIII, 132660E (20 December 2024); https://doi.org/10.1117/12.3046019

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KEYWORDS

Semantics

Climate change

Image segmentation

Network security

Unmanned aerial vehicles

Classification systems

Climatology

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