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根据细分和改进的YOLOv11模型,AR眼镜捕获的作物树叶图像上的白的计数方法

Shilong Zhao¹, Jun Lyu², Shuhua Liu³

¹School of Computer Science and Technology (School of Artificial Intelligence), Zhejiang Sci-Tech University, Hangzhou, China.

Frontiers in plant science

|October 27, 2025

概括

本研究介绍了一种使用增强现实 (AR) 眼镜和先进AI的自动化白计数系统. 该系统准确地检测和计数成年白和,改进了害虫管理策略.

科学领域:

农业昆虫学农业昆虫学
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

*白 (Bemisia tabaci) 是全球重要的农业害虫.
* 传统的手动监测是主观的,对于人口密集而言不准确,难以追踪.
*精确的白监测对于早期预警系统和有效的害虫控制至关重要.

研究的目的:

* 开发一种自动化的方法来计数成年白和晚期幼.
* 提高农业环境中白种群监测的准确性和效率.
*将图像分析与增强现实 (AR) 集成在一起,以实时可视化害虫数据.

主要方法:

* 使用增强现实 (AR) 眼镜拍摄作物叶子上的白的图像.
* 采用Mask2Former-Leaf用于初级叶片细分,以尽量减少背景噪音.
* 开发了一种YOLOv11-Whitefly检测模型,采用切片辅助超推理 (SAHI) 进行微小物体检测.
* 集成DyCM-C3K2和多分支通道重权特征金字塔网络 (MCRFPN),用于增强特征表示和融合.

主要成果:

*与主流检测模型相比,YOLOv11-Whitefly模型实现了更高的性能.
* 获得了86.20%的平均回忆率和84.25%的平均精度,用于成年白和.
* 在检测成年白和晚期幼时显示了91.60%的mAP50.

结论:

* 拟议的自动化系统显著提高了白种群监测的准确性和效率.
*与AR眼镜的集成提供了现场可视化,帮助测量员在害虫管理决策.
*这项技术为解决传统白监测方法所面临的挑战提供了强大的解决方案.

关键词:

这是一款AR眼镜.自动计数的自动计数图像细分图像细分对象检测检测对象检测对象检测一个白色的飞

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Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.