Gamification elements and student engagement in higher education using fuzzy DEMATEL analysis.

Agriculture contributes nearly 4% of global GDP and supports the livelihoods of more than 2.5 billion people worldwide, while crop diseases are estimated to cause 20–40% annual yield losses, translating into economic damages exceeding USD 220 billion per year. Leaf-based plant diseases are among the most visually detectable and economically destructive threats in crop production systems. Automated image-based diagnosis has therefore emerged as a statistically scalable solution, where deep learning models attempt to approximate a function , mapping high-dimensional image input to discrete disease class labels . However, conventional deterministic classifiers provide only point estimates , without quantifying predictive uncertainty , which is critical in high-stakes agricultural decision-making. Furthermore, empirical studies show that classification accuracy can decline by 10–25% under domain shift, particularly when models trained on controlled laboratory images are deployed in real-field environments. In this study, we adopt a framework-oriented approach, where a hierarchical vision transformer backbone is used within an uncertainty-aware evaluation pipeline to analyze performance, calibration, and robustness under domain shift. In recent years, with the rapid development of deep learning techniques, plant disease classification accuracy has been greatly improved. Convolutional Neural Networks (CNN) achieve favorable performance on widely-used benchmark dataset, surpassing 95% accuracy in the controlled setting. Nevertheless, CNN structures are locally receptive fields designed based, making use of the convolutional kernels for obtaining spatially-limited features. Although these architectures have shown excellent capability for texture-based disease identification, they might perform poorly in modeling long-range dependencies and global leaf deformations based on the level of diseases. Hierarchical Vision Transformers (ViTs), such as Swin Transformers, mitigate this limitation by learning self-attention that captures global semantic relationships among patches residing on all levels of different scales in an image. Hierarchical Vision Transformers additionally improve upon this by hierarchically aggregating patches, such that the model can learn across multi-scale representations – a necessary characteristic to be capable of modelling both localized lesion characteristics and large scale morphological deformations. However, two bottleneck problems are still open in plant disease classification. First, the vast majority of existing works are trained on single domain databases captured under controlled illumination and background clutter and perform poorly when tested on field-acquired images experiencing different lighting conditions, occlusions and background clutters. Second, the existing models work in a deterministic setting with high-confidence predictions for obscure or out-of-distribution samples. Overconfidence of this kind may result in wrong agronomic operations, economic loss and to the lack of trust for AI-based tools working inside agricultural systems. Thus, strong cross-domain generalization and uncertainty-aware prediction capabilities are crucial for real-world deployment.