利用计算机视觉实时检测作物行--在农业机器人中的应用。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Artificial Intelligence Pub Date : 2024-10-30 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1435686
Md Nazmuzzaman Khan, Adibuzzaman Rahi, Veera P Rajendran, Mohammad Al Hasan, Sohel Anwar
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引用次数: 0

摘要

实现农业机器人自主导航的目标面临着巨大挑战,主要原因是作物行图像会因天气条件和作物生长阶段而产生巨大的自然变化。检测算法的处理能力也必须大大降低,以满足实时应用的要求。为了满足上述要求,我们提出了一种具有以下特点的作物行检测算法:首先,应用投影变换来转换摄像机视图,并采用基于颜色的分割来从背景中区分作物和杂草。其次,使用聚类算法来区分作物和杂草像素。最后,采用稳健的线拟合方法来检测作物行。所提出的算法在各种不同的场景中都进行了评估,并将其功效与现有的四种不同解决方案进行了比较。该算法的总体交集大于联合(IOU)达到 0.73,在杂草丛生的挑战性场景中表现出鲁棒性。在具有挑战性场景的实时视频上进行的实验表明,我们提出的算法具有超过 90% 的检测准确率,是实时实施的可行选择。由于高精度和低推理时间,所提出的方法为农业机器人在作物田中自主导航提供了一个可行的解决方案,同时不会损坏作物,因此可作为未来研究的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-time crop row detection using computer vision- application in agricultural robots.

The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background. Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. With the high accuracy and low inference time, the proposed methodology offers a viable solution for autonomous navigation of agricultural robots in a crop field without damaging the crop and thus can serve as a foundation for future research.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
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