Robust crop disease detection using multi-domain data augmentation and isolated test-time adaptation

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. • A cross-domain framework for robust crop disease detection is proposed. • Dual augmentation using GANs and noise improves domain generalization. • FIM-guided teacher-student model avoids forgetting and adapts to new domains. • The method shows strong detection performance in diverse environments. • Selective updates ensure high accuracy in varied agricultural conditions.