对比最新AI方法在植物病虫害识别中的表现，发现视觉变压器效果最佳。
Evaluation of State-of-the-Art Deep Learning Techniques for Plant Disease and Pest Detection
- 按技术类型分类五类图像检测方法，结构清晰
- 视觉变压器准确率超99.3%，优于MobileNetV3等模型
- 适合农业AI研究者快速定位先进检测技术
防治植物病虫害对提升作物产量、减少经济损失至关重要。近年来人工智能（AI）、机器学习（ML）与深度学习（DL）的发展显著提升了图像识别的精度与效率，突破了人工识别的局限。本研究系统回顾基于计算机的植物病虫害图像检测技术，涵盖高光谱成像、非可视化方法、可视化方法、改进的深度学习架构及变压器模型。该分类体系为研究人员选择前沿检测方法提供可操作指引。综合近期研究与对比实验表明，现代AI方法在速度与准确率上持续优于传统图像分析手段。其中，层次化视觉变压器（HvT）在植物病害检测中准确率超过99.3%，优于MobileNetV3等架构。研究最后讨论系统设计挑战，提出解决方案，并展望未来研究方向。
原文摘要 · Abstract (English)
Addressing plant diseases and pests is critical for enhancing crop production and preventing economic losses. Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have significantly improved the precision and efficiency of detection methods, surpassing the limitations of manual identification. This study reviews modern computer-based techniques for detecting plant diseases and pests from images, including recent AI developments. The methodologies are organized into five categories: hyperspectral imaging, non-visualization techniques, visualization approaches, modified deep learning architectures, and transformer models. This structured taxonomy provides researchers with detailed, actionable insights for selecting advanced state-of-the-art detection methods. A comprehensive survey of recent work and comparative studies demonstrates the consistent superiority of modern AI-based approaches, which often outperform older image analysis methods in speed and accuracy. In particular, vision transformers such as the Hierarchical Vision Transformer (HvT) have shown accuracy exceeding 99.3% in plant disease detection, outperforming architectures like MobileNetV3. The study concludes by discussing system design challenges, proposing solutions, and outlining promising directions for future research.
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