葡萄主要病害检测与分级方法研究进展

Research Progress of Detection and Grading Methods for Major Grapevine Diseases

摘要:

葡萄在生长过程中面临多种病害威胁，主要包括霜霉病、白粉病、灰霉病、炭疽病和黑豆病等，目前这些病害主要通过粗放的化学药剂喷洒防治，导致土壤污染、药液残留等问题频发，造成葡萄种植园生态破坏，经济效益受损，亟需对葡萄主要病害分类分级治理。本文围绕葡萄主要病害识别、检测与分级方法，结合国内外研究现状，分别阐述葡萄主要病害潜育期检测技术和发病期检测与分级技术，包括分子生物学检测、光谱检测、成像检测技术、无人机、卫星遥感和多源数据融合等检测方法，以及基于机器学习和深度学习的病害识别分类、目标检测与分级技术。在此基础上，总结了当前面临的葡萄主要病害检测方法推广应用场景存在局限、检测与分级模型普适性不足、多模态多病害实地协同检测困难、病害动态变化全过程研究不完善、相似症状的不同病害识别精度有限等问题，并分析了主要成因。针对以上问题，归纳了近年来提出的主要病害检测算法的技术细节，对比分析了各类算法的性能表现和改进策略。最后，提出了葡萄园智能化检测未来发展方向：自然条件下面向复杂场景检测、检测与分级的普适性模型构建、小样本学习与细粒度识别研究、病症动态追踪与病斑信息预测和融合病害信息的防控装备研发等方向，为精准施药智能化治理葡萄病害奠定基础。

Abstract:

Grapevines face significant threats from various diseases during their growth cycle, including downy mildew, powdery mildew, botrytis cinerea, anthracnose, and black rot. Current reliance on extensive chemical spraying for disease control has led to persistent environmental challenges such as soil contamination and pesticide residues, resulting in ecological degradation of vineyards and economic losses. This necessitates the development of classified and hierarchical management strategies for major grape diseases. The contemporary research progress in grape disease identification, detection, and classification methodologies was systematically examined, with particular emphasis on latent period detection and symptomatic phase assessment technologies. The analysis encompassed molecular biological detection, spectral analysis, imaging techniques, unmanned aerial vehicles (UAVs), satellite remote sensing, and multi-source data fusion approaches, complemented by machine learning and deep learning-based recognition systems for disease classification, object detection, and severity grading. Critical challenges were identified in current research paradigms, including limited applicability of detection methods across diverse cultivation scenarios, insufficient generalizability of classification models, technical barriers in on-site multi-modal and multi-disease collaborative detection, incomplete understanding of dynamic pathological progression, and reduced recognition accuracy for diseases with similar symptomatic expressions. The study further elucidated the underlying causes of these limitations through comparative analysis of recent algorithmic advancements in disease detection, evaluating performance metrics and optimization strategies. Future research directions emphasized the development of intelligent detection systems for complex natural environments, establishment of generalizable detection-classification frameworks, smal-sample learning and fine-grained recognition techniques, dynamic disease progression tracking with lesion prediction capabilities, and integrated pest management equipment incorporating diagnostic data. These advancements aimed to establish a technological foundation for precision phytopharmaceutical applications and intelligent disease management in modern viticulture.