Studying weed spatial distribution patterns and implementing precise herbicide applications requires accurate weed mapping. In this study, a simple unmanned aerial vehicle (UAV) was utilized to survey 11 dry onion (Allium cepa L.) commercial fields to examine late-season weed classification and investigate weeds spatial pattern. In addition, orthomosaics were resampled to a coarser spatial resolution to simulate and examine the accuracy of weed mapping at different altitudes. Overall, 176 weed maps were generated and evaluated. Pixel and object-based image analyses were assessed, employing two supervised classification algorithms: Maximum Likelihood (ML) and Support Vector Machine (SVM). Classification processes resulted in highly accurate weed maps across all spatial resolutions tested. Weed maps contributed to three insights regarding the late-season weed spatial pattern in onion fields: 1) weed coverage varied significantly between fields, ranging from 1 to 79%; 2) weed coverage was similar within and between crop rows; and 3) weed pattern was patchy in all fields. The last finding, combined with the ability to map weeds using a low cost, off-the-shelf UAV, constitutes an important step in developing precise weed control management in onion fields.

研究杂草的空间分布模式并实施精确的除草剂应用需要精确的杂草作图。在这项研究中，一个简单的无人机（UAV）被用于调查11个干洋葱（葱属L.）商业领域，以检查季末杂草分类并调查杂草的空间格局。此外，对正马赛克进行了重新采样，以获得更粗糙的空间分辨率，以模拟和检查不同海拔高度的杂草测绘的准确性。总体上，生成并评估了176张杂草图。使用两种监督分类算法对像素和基于对象的图像分析进行了评估：最大似然（ML）和支持向量机（SVM）。分类过程可在所有测试的空间分辨率上生成高度准确的杂草图。杂草图对洋葱田中后期杂草的空间格局提供了三方面的见解：1）田间杂草的覆盖率差异很大，范围在1％到79％之间；2）作物行内和行间杂草覆盖率相似；3）在所有田地上杂草模式都不完整。