Ground-based on-line weed control using computer vision: Analyzing the inference time-accuracy dilemma

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Saad Abouzahir , Essaid Sabir , Mohamed Sadik , Mohamed Abouzahir , Fouad Agramelal
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引用次数: 0

Abstract

The detrimental effects of weeds on crop growth and yield present substantial challenges to the agribusiness sector, necessitating the deployment of robust control strategies. The rapid advancement of Computer vision (CV) techniques has driven the integration of ground-based imaging sensors to enable site-specific weed management. The main challenge in weed management revolves around on-line weed detection, which demands a careful balance between inference time and detection accuracy. Finding this balance is very important, as prioritizing a higher number of frames per second (fps) might reduce the detection precision. However, the real-time constraint for on-line weed control remains relatively unexplored. This paper addresses this gap by categorizing proposed approaches based on ground-vehicle configuration and evaluating the real-time requirements for on-line weed control. We comprehensively examine the different components of ground-vehicles including the travel speed, camera settings, and weeding tools to understand the fps required for seamless weed control operation. Results show that for travel speeds below 4kmh1, Deep Neural Networks (DNNs) operating at fps rates lower than 10 Hz are suitable for effective on-line weed detection. However, at higher speeds or with smaller Fields of View, fps demands increase. Our findings further reveal that the relatively relaxed fps requirements of on-line weed control create opportunities for deploying larger DNNs, such as NASNet-A-Large, which can significantly enhance detection accuracy. The operational latency introduced by certain weeding tools further provides additional processing time for DNNs. The continuous advancement of larger DNNs and improvements in hardware offer promising prospects for precise and effective weed management. Future research should leverage these developments, focusing on enhancing detection accuracy rather than optimizing for faster inference times, given the relaxed real-time constraints of ground-based weed control.
利用计算机视觉进行地面在线杂草控制:分析推理时间与精度的两难选择
杂草对作物生长和产量的不利影响给农业综合企业部门带来了巨大挑战,因此有必要部署强有力的控制策略。计算机视觉(CV)技术的快速发展推动了地面成像传感器的集成,从而实现了针对具体地点的杂草管理。杂草管理的主要挑战是在线杂草检测,这需要在推理时间和检测精度之间取得谨慎的平衡。找到这种平衡非常重要,因为优先考虑较高的每秒帧数(fps)可能会降低检测精度。然而,对在线杂草控制的实时性限制仍相对缺乏研究。本文针对这一空白,对基于地面车辆配置的建议方法进行了分类,并对在线杂草控制的实时性要求进行了评估。我们全面研究了地面车辆的不同组成部分,包括行驶速度、摄像头设置和除草工具,以了解无缝除草操作所需的 fps。结果表明,在行驶速度低于 4kmh-1 的情况下,深度神经网络(DNN)以低于 10 Hz 的帧频运行,适用于有效的在线杂草检测。然而,在车速较高或视场较小的情况下,帧频要求会增加。我们的研究结果进一步表明,在线杂草控制对帧速率的要求相对宽松,这为部署更大的 DNN(如 NASNet-A-Large)创造了机会,可显著提高检测精度。某些除草工具带来的操作延迟进一步为 DNN 提供了额外的处理时间。大型 DNN 的不断进步和硬件的改进为精确有效的杂草管理提供了广阔的前景。鉴于地面杂草控制的实时性限制较宽松,未来的研究应充分利用这些发展,重点提高检测精度,而不是优化更快的推理时间。
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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