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基于深度学习的目标喷雾控制在耕作阶段在小麦田中的杂草

Haiying Wang¹, Yu Chen¹, Shuo Zhang¹

¹College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China.

Frontiers in plant science

|April 11, 2025

概括

这项研究介绍了一种基于深度学习的目标喷系统,可以精确控制杂草喷. 改进的算法提高了准确性,并解决了用于实际农业应用的硬件操作歇斯底里.

科学领域:

农业工程农业工程
计算机视觉计算机视觉
机器学习机器学习

背景情况:

精准农业需要精确的杂草检测和有针对性的喷.
现有系统面临的挑战是运行歇斯底里和计算复杂性.

研究的目的:

设计和验证基于深度学习的目标喷雾控制系统.
改进YOLOv5s模型以提高杂草检测效率.
为精确的电磁控制开发一个歇斯底里算法.

主要方法:

减轻和改进对象检测的YOLOv5s模型.
设计一个目标喷雾决策和歇斯底里算法.
使用开发的系统,对模拟杂草和小麦进行了长椅实验.

主要成果:

改进的YOLOv5s模型实现了52.2%的GFLOP减少和42.4%的尺寸减少.
该模型在mAP (91.4%) 和F1 (85.3%) 中略有改善.
在不同的速度下,喷率达到99.8%,98.2%和95.7%,显示出高精度.

结论:

开发的算法为目标喷雾系统提供了出色的喷雾精度.
该系统有效地根据杂草分布区分喷.
这项研究为农业中向喷的实际应用奠定了理论基础.

关键词:

深度学习是一种深度学习.歇斯底里斯属性歇斯底里斯属性喷目标喷目标喷杂草分布的确定杂草分布的确定杂草识别标识杂草识别标识

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