发表机构
National Institute of Technology; Indian Institute of Technology, Ropar（国立技术学院; 印度理工学院罗巴尔校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述从数据中心视角系统分析植物病害数据集，建立分类法并批判性比较实验室与田间数据，揭示环境变异性和多级失衡对模型泛化的影响，提出整合环境参数的多模态监测方向，为精准农业奠定基础。
AI 中文摘要
尽管人工智能取得了快速进展，可靠的现实世界植物病害检测仍然是一个持续存在的挑战。视觉和深度学习方法已显示出有前景的结果，但它们在田间条件下的部署仍然有限。一个关键的瓶颈是依赖缺乏环境多样性、真实背景和均衡类别分布的实验室生成数据集，导致泛化能力差。相比之下，直接从农业环境中收集的数据集捕获了自然变异性，并更好地反映了农民在不同地区面临的挑战。本综述对用于植物病害检测的视觉和深度学习方法进行了批判性分析，重点放在植物病害数据集上。它建立了一个基于采集环境、可访问性、植物多样性、病害组成、类别结构和失衡严重程度的分类法，并考察了它们对模型泛化和现实世界部署的影响。对实验室和真实田间数据集的比较分析识别出阻碍病害检测的关键空白。本综述进一步分析了多级数据集失衡（包括类内、跨作物和跨数据集失衡）以及有限的环境变异性如何影响模型性能和鲁棒性，这是现有综述中未充分考察的领域。除了基于图像的方法，它强调了将环境参数（如温度、湿度、叶片湿润度）与图像数据整合以在动态田间条件下改进预测的重要性。最后，本综述识别了关于数据集构建、环境变异性、结构失衡、标准化和多模态病害监测的关键挑战、研究空白和未来方向。它为开发用于精准农业的下一代多模态框架提供了基础。
英文摘要
Despite rapid advances in artificial intelligence, reliable real-world plant disease detection remains a persistent challenge. Visual and deep learning approaches have shown promising results, but their deployment under field conditions remains limited. A key bottleneck is the reliance on laboratory-generated datasets that lack environmental diversity, realistic backgrounds, and balanced class distributions, resulting in poor generalization. In contrast, datasets collected directly from agricultural environments capture natural variability and better reflect challenges faced by farmers across regions. This review presents a critical analysis of visual and deep learning approaches for plant disease detection, with emphasis on plant disease datasets. It establishes a taxonomy based on acquisition setting, accessibility, plant diversity, disease composition, class structure, and imbalance severity, and examines their implications for model generalization and real-world deployment. A comparative analysis of laboratory and real-field datasets identifies critical gaps that hinder disease detection. The review further analyzes how multi-level dataset imbalance, including intra-class, inter-crop, and cross-dataset imbalance, and limited environmental variability affect model performance and robustness, an area insufficiently examined in existing surveys. Beyond image-based approaches, it highlights the importance of integrating environmental parameters such as temperature, humidity, and leaf wetness with image data to improve prediction under dynamic field conditions. Finally, the review identifies key challenges, research gaps, and future directions concerning dataset construction, environmental variability, structural imbalance, standardization, and multimodal disease monitoring. It provides a foundation for developing next-generation multimodal frameworks for precision agriculture.
