首页|Robust crop disease detection using multi-domain data augmentation and isolated test-time adaptation
Robust crop disease detection using multi-domain data augmentation and isolated test-time adaptation
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NETL
NSTL
Elsevier
Crop diseases present a critical threat to global food security, with traditional methods for disease detection often relying on manual diagnosis, which is labor-intensive and requires specialized expertise. While deep learning provides promising avenues for automated disease detection, many existing models are challenged by domain shift, performing poorly when applied to data from new environments with different distributions. To address this issue, we propose a novel cross-domain detection framework that integrates Multi-Domain Data Augmentation and Isolated Test-Time Adaptation Optimization (ITTA) to enhance model generalization and robustness in dynamic agricultural settings. Our approach begins with Multi-Domain Data Augmentation, which combines strong and weak augmenters to generate diverse cross-domain images. The strong augmenter, based on Generative Adversarial Networks (GANs), produces varied images that preserve class consistency, while the weak augmenter applies Gaussian noise and brightness adjustments to make the model more resilient to minor environmental changes. This dual augmentation strategy enables the model to learn domain-invariant features and improves its generalization capability across different domains. The second component ITTA, employs a teacher-student model structure that leverages the Fisher Information Matrix (FIM) to isolate domain-sensitive and domain-invariant parameters. During test-time adaptation, only the domain-sensitive parameters in the student model are updated, allowing the model to adapt to new domain-sensitive features while preserving essential knowledge from the source domain. This selective update process prevents catastrophic forgetting and maintains high detection accuracy under varying environmental conditions. Experimental results demonstrate that our framework significantly enhances model performance across diverse settings, offering a robust foundation for intelligent and precise crop disease detection, which is essential for improving agricultural productivity and food security.
Adaptive disease detectionSelective parameter updatingFisher Information MatrixSmart agricultureUncertainty estimation
Rui Fu、Jiao Han、Yumei Sun、Shiyu Wang、Mohammed Abdulhakim Al-Absi、Xuewei Wang、Hao Sun
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Shandong Province University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262700, Shandong, China
Department of Smart Computing, Kyungdong University, Giosung, 24764, Gangwon-do, Republic of Korea
NETL
NSTL
Elsevier
