基于视觉语言转换器的番茄叶病检测用于便携式温室监测设备
Manveen Kaur1, Rajmeet Singh2, Shahpour Alirezaee1
1Department of Mechanical Engineering, University of Windsor, Windsor, Canada.
Plant methods
|October 29, 2025
概括
一个新的番茄叶病视觉语言模型 (TLDVLM) 准确地使用先进的AI识别了10种番茄病. 该工具增强了作物监测,并支持全球粮食安全工作.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 番茄叶病严重影响全球粮食安全,需要先进的检测方法.
- 现有的诊断工具往往缺乏大规模农业应用所需的准确性和效率.
研究的目的:
- 引入一种新的番茄叶病视觉语言模型 (TLDVLM),用于精确分类10种不同的番茄叶病.
- 利用人工智能开发一个高效和用户友好的农业诊断工具.
主要方法:
- 使用了BLIP-2架构,并增强了低级别调整 (LoRA) 以进行参数高效的微调.
- 集成接地DINO用于叶子检测和SAM-2用于图像预处理期间的像素级细分.
- 开发了用于疾病推断,信息检索和PDF摘要生成的实用应用程序.
主要成果:
- 实现了高性能指标:97.27%的准确性,0.9587的精度,0.9789的回忆力和0.9681的F1分数.
- 与基线模型 (如CLIP-LoRA和ConvNeXT-tiny) 相比,表现出更高的性能.
- 成功地将模型检查点集成到一个功能应用程序中,用于现实世界.
结论:
- 该TLDVLM,利用LoRA适应的视觉语言模型,为番茄叶病的检测提供了一个高度准确和高效的解决方案.
- 开发的应用程序提供了一个用户友好的界面,用于诊断疾病和访问相关信息,支持农业实践.
- 这项研究为在农业中开发先进的人工智能驱动的诊断工具铺平了道路,以加强粮食安全.
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