YOLOv11-AIU:一种轻量级检测模型,用于对番茄的早期虫病进行分类检测

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Xiuying Tang¹, Zhongqing Sun¹, Linlin Yang¹

¹College of Mechanical and Electrical Engineering, Yunnan Agricultural University, Kunming, 650201, China.

|August 25, 2025

概括

一个新的AI模型,YOLOv11-AIU,可以准确地检测和评分番茄病的早期严重程度. 这种先进的物体检测系统通过早期,可靠的农作物疾病识别来改善精准农业.

科学领域:

植物病理学
计算机视觉
农业技术

背景情况:

番茄早期腐烂 (Alternaria solani) 显著降低了作物的产量.
目前的检测方法与微妙的,低对比度或多尺度的病变相斗争,
精确的严重程度分级对于有效的疾病管理至关重要.

研究的目的:

开发一种轻量级物体检测模型,用于准确分类番茄早期腐烂的严重程度.
增强特征表示并保留细微的损伤细节以改善检测.
实现实践,现场应用的实时性能.

主要方法:

开发了YOLOv11-AIU,一个增强的YOLOv11框架,包含C3k2_iAFF注意力融合和Adown多分支下载样本.
整合统一的IoU损失用于精确的边界盒回归.
创建并扩展一个六层的注释数据集到5000张图像.

主要成果:

YOLOv11-AIU获得了高性能:mAP@50达到94. 1%和mAP@50-95达到93. 4%.
该模型的推断速度为15.67 FPS.
在Luban Cat5平台上确认了实时性能,优于YOLOv3-tiny,YOLOv8n和SSD.

结论:

YOLOv11-AIU提供了卓越的准确性和效率,用于早期检测番茄虫害和严重程度分类.
该模型显示了将其纳入精准农业和智能植物健康监测系统的巨大潜力.
这种以人工智能为驱动的方法可促进早期和可靠的疾病识别,支持可持续的作物生产.

关键词:

疾病严重程度分级嵌入式部署轻量级物体检测番茄的早期腐烂没有.

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