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在植物疾病识别中提高异常检测

Jiuqing Dong^1,2,3, Heng Zhou², Alvaro Fuentes^2,3

¹School of Computer and Information Engineering, Institute for Artificial Intelligence, Shanghai Polytechnic University, Shanghai, China.

Frontiers in plant science

|September 2, 2025

概括

这项研究引入了一种开放式植物疾病识别方法,以识别新型疾病. 我们的综合知识方法显著提高了异常检测,改善了农业疾病管理.

科学领域:

农业科学
计算机视觉
机器学习

背景情况:

植物疾病威胁全球粮食安全和公共健康.
目前的植物疾病识别方法仅限于封闭的场景,无法检测出新型疾病.
实际应用需要能够识别已知和未知的植物病的系统.

研究的目的:

将植物疾病的识别扩展到一个开放的场景.
对卷积神经网络 (CNN),视觉转换器 (ViT) 和视觉语言模型 (VLM) 的异常检测性能进行比较.
提出一种新的知识集方法,以改善开放式植物疾病的识别.

主要方法:

在各种微调策略下对CNN,ViT和VLM进行基准异常检测.
开发综合知识方法,整合通用知识和特定领域的知识.
在logit和功能空间中整合知识以提高模型性能.

主要成果:

拟议的知识组合方法在开放式植物疾病识别方面显著优于现有的基线.
虚假阳性率降低到95%的真阳性率 (FPR@TPR95),从VLM的43.88%降至7.05%.
在全射设置中,FPR@TPR95从15.38%进一步降低到0.71%.

结论:

知识整体方法在各种模型架构和培训范式中表现出强度和通用性.
这种方法有助于识别已知和未知的植物病,这对于实际农业应用至关重要.
这项研究为人工智能驱动的植物疾病管理系统铺平了道路.

关键词:

异常检测几次性学习知识融合植物疾病的识别转移学习

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