Khan Md Nazmuzzaman, Rahi Adibuzzaman, Rajendran Veera P, Al Hasan Mohammad, Anwar Sohel
Lead Research Scientist (Kroger), 84.51°, Cincinnati, OH, United States.
Mechatronics and Autonomous Research Lab, Purdue University, Mechanical Engineering, Indianapolis, IN, United States.
Front Artif Intell. 2024 Oct 30;7:1435686. doi: 10.3389/frai.2024.1435686. eCollection 2024.
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.
实现农业机器人自主导航的目标面临着重大挑战，这些挑战主要源于天气条件和作物生长阶段导致的作物行图像的大量自然变化。对于实时应用而言，检测算法的处理速度也必须显著降低。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相机视角，并采用基于颜色的分割方法将作物和杂草与背景区分开来。其次，使用聚类算法区分作物像素和杂草像素。最后，实施一种稳健的直线拟合方法来检测作物行。所提出的算法在各种不同场景下进行了评估，并与四种不同的现有解决方案进行比较来评估其有效性。该算法实现了0.73的总体交并比（IOU），并且在杂草生长旺盛的具有挑战性的场景中表现出稳健性。在具有挑战性场景的实时视频上进行的实验表明，我们提出的算法检测准确率超过90%，是实时实现的可行选择。凭借高精度和低推理时间，所提出的方法为作物田间农业机器人的自主导航提供了一种可行的解决方案，且不会损害作物，因此可作为未来研究的基础。
