Accurate and timely detection of crop diseases is essential for global food security and sustainable agriculture. This review provides a comprehensive analysis of recent advancements in leaf image-based crop disease detection using machine learning, deep learning (DL), convolutional neural networks, vision transformers, and emerging state-space models. Following PRISMA guidelines, the review systematically examines peer-reviewed studies (2021–2025) and evaluates datasets, model architectures, validation protocols, and deployment constraints. Unlike previous surveys, this review uniquely integrates explainable artificial intelligence (XAI) with emphasis on physiology-based interpretability and real-world field usability. Key findings reveal that while DL models achieve > 95% accuracy on controlled datasets like PlantVillage, performance degrades significantly under field conditions due to dataset bias, domain shifts, and multi-disease complexity. Critical research gaps are identified, including insufficient annotation standards, limited cross-domain validation, and computational constraints for edge deployment. Emerging directions include agriculture-specific foundation models, domain-generalizable vision systems, lightweight mobile strategies, and next-generation XAI frameworks aligned with plant physiology. By bridging technical advancements with agronomic interpretability, this work provides researchers and practitioners with a roadmap for developing trustworthy, field-deployable AI systems for sustainable crop health management. Graphical abstract The alternative text for this image may have been generated using AI.

Detecting brain tumors can be challenging as a clinical problem because of tumor heterogeneity and reliance on manual neuroimaging interpretation, which can be prone to human error. Artificial intelligence (AI) has shown strong potential as a clinical decision-support tool, assisting radiologists in improving diagnostic accuracy and supporting the interpretation of neuroimaging data. AI using machine learning (ML) and deep learning (DL) algorithms has performed credibly in tumor detection, segmentation, and classification tasks. Challenges such as dataset bias, limited generalization, lack of explainability, and high computational costs must be addressed before clinical application. This article provides a comprehensive review of AI methods applied to brain tumor imaging, with a primary focus on adult diffuse gliomas and secondary coverage of brain metastases, meningiomas, and pediatric tumors where relevant. The major contribution of this review is a new three-factor (diagnostic tasks, learning strategies, and data modalities) taxonomy. Beyond accuracy-based metrics, we provide a qualitative assessment of robustness, generalization, and the principal barriers to clinical adoption identified in the published literature, while acknowledging that comprehensive clinical utility evidence remains an open research direction.

Foreign language speaking anxiety (FLSA) remains a widespread problem in the spoken development of EFL learners. This cross-sectional study examined the associations between three AI-dialogic scaffolding interventions (GenAI Chatbots, digital storytelling, and collaborative reasoning), the speaking anxiety and confidence of Saudi EFL learners, with self-efficacy as a mediating variable. A sample of 287 valid responses (68.3% response rate) from Saudi undergraduate EFL students was collected through a cross-sectional survey and analysed using partial least squares structural equation modelling (PLS-SEM). The findings confirmed significant associations between the three scaffolds and reduced speaking anxiety and increased speaking confidence, both directly and indirectly, through the mediation of enhanced self-efficacy. Digital Storytelling showed the strongest direct association with anxiety reduction, while GenAI Chatbots showed the strongest association with self-efficacy. Due to the cross-sectional design, causal claims cannot be inferred from these associations; the results reflect covariance among perceptions at a single time point. The findings provide a validated structural model indicating that self-efficacy is a significant psychological process. The study offers implications for developing affective-oriented, technology-based EFL programmes, though longitudinal research is needed to establish causal direction.