基于截距密度聚类的航拍影像作物行中心线提取方法

Crop Row Centerline Extraction from Aerial Images Based on Intercept Density Clustering

摘要:

通过无人机航拍影像快速提取作物行中心线对农机行间喷药、除草和收获等导航作业任务具有重要意义。本研究以新疆地区大田苗期棉花为研究对象，提出了一种基于截距密度聚类的航拍影像作物行提取方法。首先，对航拍影像应用过绿颜色特征提取算法、最大类间方差算法和影像分割投影法获取作物行特征点集。其次，基于截距密度的聚类算法对特征点集进行作物行聚类分割。最后，采用最小二乘法拟合每垄作物行中心线并投影反算到WGS-84坐标系，为农机GNSS行间导航作业提供路径信息。以RTK-GNSS接收机的高精度实地采样数据为参照，对大规模棉田作物行识别定位方法进行了试验验证。结果表明，分别采用分割间距为15、45、75、105像素的分割投影法提取特征点拟合作物行中心线，横向偏差平均值最大值分别为0.022、0.024、0.025、0.112m，横向偏差标准差最大值分别为0.027、0.028、0.028、0.032m；角度偏差平均值分别为0.004°、0.003°、0.002°、0.005°，角度偏差标准差分别为0.002°、0.001°、0.001°、0.002°；提取中心线耗时分别为348.35、101.93、76.29、63.33s。综合考虑精度和效率，分割间距取75像素为较优选择。该方法适合无人机航拍大规模棉田作物行识别和定位，可为棉花生产智能化田间管理和收获环节行间导航作业提供充分的作业路径信息。

Abstract:

Rapid extraction of crop row centerlines from drone aerial imagery is crucial for navigating agricultural tasks such as inter-row spraying, weeding, and harvesting. Focusing on cotton seedlings in Xinjiang’s field crops, an aerial image-based crop row extraction method was proposed by using intercept density clustering. Firstly, aerial images underwent green color feature extraction, maximum inter-class variance analysis, and image segmentation projection to obtain feature point sets for crop rows. Secondly, the feature point set underwent crop row clustering and segmentation by using an intercept-density-based clustering algorithm. Finally, the centerlines of each crop row were fitted by using the least squares method and projected back to the WGS-84 coordinate system, providing path information for GNSS-based inter-row navigation operations of agricultural machinery. Field experiments using high-precision RTK-GNSS receiver data as reference validated this large-scale cotton field crop row identification and positioning method. Results indicated that when applying segmentation projection methods with pixel spacing of 15 pixels, 45 pixels, 75 pixels, and 105 pixels to extract feature points for crop row centerline fitting, the maximum average lateral deviation values were 0.022m, 0.024m, 0.025m, and 0.112 m, respectively. The maximum standard deviations for lateral deviation were 0.027m, 0.028m, 0.028m, and 0.032m;the average angular deviations were 0.004°, 0.003°, 0.002°, and 0.005°, respectively, with standard deviations of 0.002°, 0.001°, 0.001°, and 0.002°;the time required for centerline extraction was 348.35s, 101.93s, 76.29s, and 63.33s. Considering both accuracy and efficiency, a segmentation interval of 75 pixels was the optimal choice. This method was suitable for large-scale crop row identification and positioning in cotton fields using drone aerial imagery, providing sufficient path information for mechanized field management and row-based navigation during cotton harvesting operations.