Autonomous diode laser weeding mobile robot in cotton field using deep learning, visual servoing and finite state machine

IF 3.5 Q1 AGRONOMY Frontiers in Agronomy Pub Date : 2024-05-16 DOI:10.3389/fagro.2024.1388452
Canicius J. Mwitta, Glen C. Rains, Eric Prostko
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Abstract

Small autonomous robotic platforms can be utilized in agricultural environments to target weeds in their early stages of growth and eliminate them. Autonomous solutions reduce the need for labor, cut costs, and enhance productivity. To eliminate the need for chemicals in weeding, and other solutions that can interfere with the crop’s growth, lasers have emerged as a viable alternative. Lasers can precisely target weed stems, effectively eliminating or stunting their growth. In this study an autonomous robot that employs a diode laser for weed elimination was developed and its performance in removing weeds in a cotton field was evaluated. The robot utilized a combination of visual servoing for motion control, the Robotic operating system (ROS) finite state machine implementation (SMACH) to manage its states, actions, and transitions. Furthermore, the robot utilized deep learning for weed detection, as well as navigation when combined with GPS and dynamic window approach path planning algorithm. Employing its 2D cartesian arm, the robot positioned the laser diode attached to a rotating pan-and-tilt mechanism for precise weed targeting. In a cotton field, without weed tracking, the robot achieved an overall weed elimination rate of 47% in a single pass, with a 9.5 second cycle time per weed treatment when the laser diode was positioned parallel to the ground. When the diode was placed at a 10°downward angle from the horizontal axis, the robot achieved a 63% overall elimination rate on a single pass with 8 seconds cycle time per weed treatment. With the implementation of weed tracking using DeepSORT tracking algorithm, the robot achieved an overall weed elimination rate of 72.35% at 8 seconds cycle time per weed treatment. With a strong potential for generalizing to other crops, these results provide strong evidence of the feasibility of autonomous weed elimination using low-cost diode lasers and small robotic platforms.
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利用深度学习、视觉伺服和有限状态机实现棉田自主二极管激光除草移动机器人
小型自主机器人平台可用于农业环境,在杂草生长的早期阶段将其清除。自主解决方案减少了对劳动力的需求,降低了成本,提高了生产率。为了消除除草过程中对化学品的需求以及其他可能干扰作物生长的解决方案,激光已成为一种可行的替代方案。激光可以精确瞄准杂草茎干,有效地消除或抑制其生长。本研究开发了一种利用二极管激光清除杂草的自主机器人,并对其清除棉田杂草的性能进行了评估。该机器人利用视觉伺服系统进行运动控制,并结合机器人操作系统(ROS)的有限状态机实现(SMACH)来管理其状态、行动和转换。此外,机器人还利用深度学习进行杂草检测,并结合全球定位系统和动态窗口方法路径规划算法进行导航。机器人利用其二维笛卡尔臂,将激光二极管连接到旋转云台机构上,以精确定位杂草。在一块棉田里,在没有杂草跟踪的情况下,当激光二极管与地面平行放置时,机器人单次除草的总体除草率达到 47%,每次除草的周期时间为 9.5 秒。当二极管与水平轴成 10° 向下倾斜时,机器人单次清除杂草的总比率达到 63%,每次处理杂草的周期时间为 8 秒。在使用 DeepSORT 跟踪算法实施杂草跟踪后,机器人在每次处理杂草的 8 秒周期时间内实现了 72.35% 的总体杂草清除率。这些结果为使用低成本二极管激光器和小型机器人平台自主清除杂草的可行性提供了强有力的证据,具有推广到其他作物的巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Frontiers in Agronomy
Frontiers in Agronomy Agricultural and Biological Sciences-Agricultural and Biological Sciences (miscellaneous)
CiteScore
4.80
自引率
0.00%
发文量
123
审稿时长
13 weeks
期刊最新文献
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