A systematic review of deep learning and super resolution techniques for leaf level and canopy level plant disease detection

Plant diseases are a serious issue that cause food shortages and financial losses. In large-scale farming, traditional disease detection techniques that primarily rely on expert inspection are frequently unfeasible. Deep learning and super-resolution methods for plant disease detection are thoroughly reviewed in this study, with an emphasis on their use in leaf-level imaging and UAV-based monitoring. This thorough review was carried out using the PRISMA framework, looking at peer-reviewed publications from important databases like Google Scholar, ScienceDirect, Web of Science, and Scopus. According to the review, low spatial resolution, environmental variability, occlusion, and domain shift cause convolutional and transformer-based models to perform poorly at canopy and field scales, despite achieving high accuracy on leaf-level datasets. Despite improving the perceptual quality of aerial imagery, super-resolution techniques are still difficult to incorporate into disease detection pipelines because of their high computational overhead, lack of task-aware training, scarcity of annotated UAV datasets, and poor generalization in real-world scenarios. There are few actual architectural implementations of cross-scale integration strategies currently in use; most of them are conceptual. Deep learning for plant disease detection has come a long way, but reliably deploying this technology outside of controlled environments remains challenging due to scale differences and data availability constraints. Coordinated developments in cross-scale learning approaches, data collection, and model design are needed to address these issues. The results also emphasize the significance of multimodal data fusion, super-resolution-assisted domain adaptation, and hierarchical transfer learning as viable approaches to enhancing the scalability and dependability of plant health monitoring systems. This review highlights useful research directions and provides a critical overview of current methods. It encourages the development of AI-powered solutions for better crop management, early disease detection, and sustainable farming methods.