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基于改进的YOLOv8n,增强多层次的茶叶识别

Xinchen Tang¹, Li Tang², Junmin Li¹

¹School of Mechanical Engineering, Xihua University, Chengdu, China.

Frontiers in plant science

|April 14, 2025

概括

一个新的茶你只看一次v8n (T-YOLOv8n) 模型通过改进茶叶识别来增强自动茶采摘. 这种先进的深度学习方法可以提高复杂茶园的精度和效率.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

自动茶叶采摘需要精确的茶叶识别,以提高效率和质量.
深度学习在茶叶检测方面表现有前途,但多层复合特征分析缺乏.
现有的方法难以识别不同的茶叶类别和重叠的目标.

研究的目的:

为了提高不同茶叶类别的识别精度,用于自动采摘.
开发一个强大的深度学习模型,能够检测小型和重叠的茶叶目标.
为智能茶叶种植创造一个高效和可部署的解决方案.

主要方法:

提出了一种创新方法,用于生成重叠标签茶类数据集.
推出了茶你只看一次v8n (T-YOLOv8n) 模型用于多层复合茶叶检测.
集成卷积块注意模块 (CBAM) 和双向特征金字塔网络 (BiFPN) 用于特征融合.
利用CIOU和焦点损失功能来优化界限框预测.

主要成果:

T-YOLOv8n模型在检测小型和重叠的茶叶目标方面取得了卓越的性能.
与YOLOv8,YOLOv5和YOLOv9.9相比,在mAP50中实现了精度从70.5%提高到74.4%,并从73.3%提高到75.4%.
计算成本降低了高达19.3%,证明了效率.
展示了对各种照明和背景变化的增强适应能力.

结论:

T-YOLOv8n模型为自动茶叶采摘提供了更好的检测精度和计算效率.
该模型的稳定性和适应性使其适合在资源有限的边缘计算环境中实际部署.
这项研究通过推进智能茶叶分类和自动收获,促进茶叶生产效率和可持续性,为智能农业做出了贡献.

关键词:

YOLOv8 的改进有效的功能是融合融合.功能损失的功能损失的功能.智能农业智能农业茶的识别功能茶的识别功能

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