病害识别是计算机视觉技术在农业领域的重要应用之一，对及时发现和早期预防植物病害起着关键作用。近年来，随着病害识别方法的不断演进，病害识别性能有了显著提高，但自然条件下病害特征提取困难、病害严重程度难以区分等问题依然存在。为了在现有方法的基础上进一步探索病害识别的新思路，先是针对不同识别目标，分析病害识别和病害严重程度识别的研究现状。然后从视觉特征类型和学习方式两个角度对植物病害识别方法进行全面的比较与研究，指出深度模型是当前植物病害识别的主流方法，融合多源信息和结合不同的机器学习方式是改进植物病害识别的重要手段，并将不同识别方法在主流数据集上的性能进行对比和分析。最后对未来发展方向进行展望。

Disease identification is one of the important applications of computer vision technology in agriculture, playing a crucial role in timely detection and early prevention of plant diseases. In recent years, with the continuous evolution of disease identification methods, there has been a significant improvement in disease recognition performance. However, challenges still exist in extracting disease characteristics under natural conditions and differentiating disease severity. To explore new approaches for disease identification beyond existing methods, the current research status of disease identification and disease severity identification was analyzed. Furthermore, a comprehensive comparison and study of plant disease recognition techniques were conducted, considering visual feature types and learning methods. It is pointed that deep modeling is the mainstream approach for plant disease identification. Combining multiple sources of information and utilizing different machine learning techniques are important means to improve plant disease recognition. The performance of different recognition methods was evaluated and analyzed using popular datasets. Finally, future development directions were outlined.