Recent advances in plant disease detection: challenges and opportunities.

ABSTRACT: Plant diseases cause approximately 220 billion USD in annual agricultural losses, driving demand for automated detection systems. This systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging, examining their evolution from classical image processing to modern neural architectures. We evaluate state-of-the-art models across 11 benchmark datasets, revealing significant performance gaps between laboratory conditions (95-99% accuracy) and field deployment (70-85% accuracy). Transformer-based architectures demonstrate superior robustness, with SWIN achieving 88% accuracy on real-world datasets compared to 53% for traditional CNNs. Our analysis identifies three critical deployment constraints: environmental variability sensitivity, econ

SUBMITTER: Shafay M 

PROVIDER: S-EPMC12570820 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature