Revolutionizing plant disease diagnosis through vision-based intelligence and next-generation computing

Plant diseases significantly threaten global agriculture by reducing crop quality and yield. Early and accurate detection is vital to mitigate these impacts and ensure food security. This review presents a comprehensive survey of vision-based machine learning (ML) and deep learning (DL) approaches for plant disease detection. This review introduces a comparative analysis of more than 25 benchmark datasets and categorizes the progression from traditional ML methods to advanced DL models such as CNN, GAN, and Vision Transformers. This paper also addresses practical challenges such as image noise, environmental variability, and the domain gap between controlled and real-world datasets. Furthermore, the review explores the integration of Large Language Models (LLMs) into plant disease monitoring pipelines for annotation assistance, real-time farmer interaction, and multimodal reasoning. Moreover, the study emphasizes mobile and edge AI applications, including smartphone-based tools, AR interfaces, and IoT-enabled monitoring, enhancing accessibility for farmers in resource-constrained environments. A novel gap analysis and research roadmap are proposed to differentiate from existing works, outlining a future AI-driven agricultural ecosystem. This review concludes by identifying critical challenges and offering actionable research directions for robust, scalable, and interpretable plant disease detection systems in real-world agricultural settings.