Applications of transfer learning in sunflower disease detection: advances, challenges, and future directions

The review revealed substantial progress in TL applications for the diagnosis of sunflower disease but underscored the need for larger, standardized datasets and cross-regional validation. Future studies should prioritize interpretable, adaptive architectures that can function in real-world agricultural environments. The insights drawn from this synthesis extend beyond sunflower pathology, offering a foundation for scalable, domain-transferable TL solutions in broader plant disease detection contexts.