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使用机器人技术和深度学习 (YOLOv7) 针对帕尔默花的杂草管理

Amlan Balabantaray¹, Shaswati Behera¹, CheeTown Liew¹

¹Machine Automation and Agricultural Robotics Laboratory, Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, United States.

Frontiers in robotics and AI

|October 29, 2024

概括

一个支持人工智能的机器人系统精确地准了像棕杏仁这样的杂草,为传统除草剂应用提供了一个可持续的替代方案. 这一创新减少了农业对环境的影响和作物损害.

科学领域:

农业工程农业工程
计算机科学计算机科学
农业学是一种农业学.

背景情况:

有效的杂草管理对于作物产量和可持续性至关重要.
传统的除草剂应用方法缺乏精度,可能会对环境造成伤害.
棕花 (Amaranthus palmeri S. Watson) 是内布拉斯加州农业中一个非常有问题的杂草.

研究的目的:

开发和演示一个支持人工智能的机器人系统,用于有针对性的杂草管理.
提高农业作物中除草控制的精度和可持续性.
为了特别应对帕尔默花染的挑战.

主要方法:

开发一个全的人工智能机器人系统,包括视觉,硬件,软件和机器人平台.
实现YOLOv7,一个深度学习对象检测模型,用于实时杂草识别.
集成基于门架的喷雾系统,用于精确的点喷.

主要成果:

支持人工智能的除草机器人在识别帕尔默花时平均达到60.4%的精度和62%的回忆率.
该系统成功地在不同生长阶段实时识别了杂草.
机器人系统在受控的户外条件下有效地运行.

结论:

支持人工智能的机器人系统为有针对性的杂草管理提供了精确和可持续的解决方案.
这项技术为传统的,不太歧视除草剂应用方法提供了可行的替代方案.
开发的除草机器人显示了改善农业实践和减少环境影响的巨大潜力.

关键词:

这就是YOLOv7的意思.人工智能的人工智能是人工智能.深度学习是一种深度学习.机器视觉机器视觉机器视觉实时实时的时间.农业中的机器人和自动化有针对性的喷.杂草管理杂草管理

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