一个安卓智能手机应用程序,用于使用轻量级YOLO网络检测大米和识别大米生长阶段
Huiwen Zheng1, Changjiang Liu1, Lei Zhong1
1College of Electronic Engineering, South China Agricultural University, Guangzhou, Guangdong, China.
Frontiers in plant science
|May 1, 2025
概括
这项研究介绍了YOLO_ECO,这是一种用于移动大米表型化的深度学习模型,在检测大米和生长阶段方面达到高精度. YOLO-RPD应用程序为精准农业提供了一个实用的工具,增强了谷物产量管理.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 精确的田间管理对于最大限度地提高大米谷物产量至关重要.
- 准确地检测大米和识别生长阶段是精准农业的关键组成部分.
研究的目的:
- 探索深度学习在手机上用于米表型应用的应用.
- 开发一种高效的深度学习模型,用于实时识别大米生长阶段和恐慌检测.
主要方法:
- 提出了一个改进的YOLOv8模型,命名为YOLO_Efficient计算优化 (YOLO_ECO).
- 关键的修改包括C2f-FasterBlock-Effective Multi-scale Attention (C2f-Faster-EMA) 模块,Slim Neck,以及轻量级共享卷积检测 (LSCD) 头部.
- 一个安卓应用程序,YOLO-RPD,为移动部署而开发.
主要成果:
- YOLO_ECO实现了高平均精度:96.4% (启动),93.2% (标题) 和81.5% (填充).
- 该模型在检测封闭和小恐慌时表现出卓越的性能.
- YOLO_ECO显著优化了参数数量,计算需求和模型大小,其mAP为90.4%和1.8M参数.
结论:
- YOLO-RPD应用程序证明了在移动设备上部署深度学习模型的可行性,用于精密农业.
- 这为米种植者提供了一个实用的,轻量级的工具,用于实时监测和改进田间管理.
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