Due to the increase in the use of precision agriculture, field trials have increased in size to allow for genomic selection tool development by linking quantitative phenotypic traits to sequence variations in the DNA of various crops. Crop row detection is an important step to enable the development of an efficient downstream analysis pipeline for genomic selection. In this paper, an efficient crop row detection algorithm was proposed that detected crop rows in colour images without the use of templates and most other pre-information such as number of rows and spacing between rows. The method only requires input on field weed intensity. The algorithm was robust in challenging field trial conditions such as variable light, sudden shadows, poor illumination, presence of weeds and noise and irregular crop shape. The algorithm can be applied to crop images taken from the top and side views. The algorithm was tested on a public dataset with side view images of crop rows and on Genomic Sub-Selection dataset in which images were taken from the top view. Different analyses were performed to check the robustness of the algorithm and to the best of authors’ knowledge, the Receiver Operating Characteristic graph has been applied for the first time in crop row detection algorithm testing. Lastly, comparing this algorithm with several state-of-the-art methods, it exhibited superior performance.

中文翻译：

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一种基于无模板机器视觉的作物行检测算法

由于精准农业使用的增加，田间试验的规模有所增加，通过将定量表型性状与各种作物的 DNA 序列变异联系起来，允许开发基因组选择工具。作物行检测是开发用于基因组选择的高效下游分析管道的重要步骤。在本文中，提出了一种高效的作物行检测算法，该算法在不使用模板和大多数其他预信息（如行数和行间距）的情况下检测彩色图像中的作物行。该方法只需要输入田间杂草强度。该算法在具有挑战性的田间试验条件下是稳健的，例如可变光、突然阴影、照明差、杂草和噪音的存在以及不规则的作物形状。该算法可应用于裁剪从顶视图和侧视图拍摄的图像。该算法在具有作物行侧视图图像的公共数据集和从顶视图获取图像的基因组子选择数据集上进行了测试。进行了不同的分析以检查算法的稳健性，据作者所知，接收器操作特征图已首次应用于作物行检测算法测试。最后，将该算法与几种最先进的方法进行比较，它表现出优越的性能。进行了不同的分析以检查算法的稳健性，据作者所知，接收器操作特征图已首次应用于作物行检测算法测试。最后，将该算法与几种最先进的方法进行比较，它表现出优越的性能。进行了不同的分析以检查算法的稳健性，据作者所知，接收器操作特征图已首次应用于作物行检测算法测试。最后，将该算法与几种最先进的方法进行比较，它表现出优越的性能。