Accurate weed mapping in early growing season is an essential step in the site-specific weed management system. This study focuses on validating the potential application of high resolution multispectral and thermal UAS images in classification of weed species and glyphosate-resistant weeds at early phenological stages. A field experiment was conducted to evaluate supervised classification methods to identify three-weed species including waterhemp (Amaranthus rudis), kochia (Kochia scoparia), and ragweed (Ambrosia artemisiifolia L.). The accuracy of six classification algorithms namely Parallelepiped, Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Support Vector Machine and Decision Tree implemented at pixel and object-based levels in weed species classification were evaluated. Thermal infrared imagery was also used to assess the canopy temperature variance within the weed species to identify the glyphosate-resistance status in detected weeds. The object-based algorithms developed with mosaicked imagery effectively classified weed species with the overall accuracy and Kappa coefficient values greater than 86% and 0.77, respectively. The lowest accuracy and Kappa coefficient (67% and 0.58) were observed for pixel-based Mahalanobis Distance algorithm. The canopy temperature-based classification of susceptible and resistant weeds resulted in the discrimination accuracies of 88%, 93% and 92% in glyphosate-resistant kochia, waterhemp and ragweed, respectively.

中文翻译：

---

在生长早期使用高分辨率图像对杂草种类和抗草甘膦杂草进行田间识别

在早期生长季节进行准确的杂草测绘是特定地点杂草管理系统中必不可少的一步。本研究的重点是验证高分辨率多光谱和热 UAS 图像在早期物候阶段杂草种类和抗草甘膦杂草分类中的潜在应用。进行了田间试验以评估监督分类方法，以识别三种杂草物种，包括水麻 (Amaranthus rudis)、地肤 (Kochia scoparia) 和豚草 (Ambrosia artemisiifolia L.)。评估了六种分类算法的准确性，即平行六面体、马氏距离、最大似然、光谱角度映射器、支持向量机和决策树在杂草物种分类中在像素和基于对象的级别实施。热红外图像还用于评估杂草种类内的冠层温度变化，以确定检测到的杂草的草甘膦抗性状态。使用镶嵌图像开发的基于对象的算法对杂草种类进行了有效分类，总体准确度和 Kappa 系数值分别大于 86% 和 0.77。对于基于像素的 Mahalanobis 距离算法，观察到最低准确度和 Kappa 系数（67% 和 0.58）。基于冠层温度的敏感杂草和抗性杂草分类导致对草甘膦抗性地肤、水麻和豚草的识别准确率分别为88%、93%和92%。使用镶嵌图像开发的基于对象的算法对杂草种类进行了有效分类，总体准确度和 Kappa 系数值分别大于 86% 和 0.77。对于基于像素的 Mahalanobis 距离算法，观察到最低准确度和 Kappa 系数（67% 和 0.58）。基于冠层温度的敏感杂草和抗性杂草分类导致对草甘膦抗性地肤、水麻和豚草的识别准确率分别为88%、93%和92%。使用镶嵌图像开发的基于对象的算法对杂草种类进行了有效分类，总体准确度和 Kappa 系数值分别大于 86% 和 0.77。对于基于像素的 Mahalanobis 距离算法，观察到最低准确度和 Kappa 系数（67% 和 0.58）。基于冠层温度的敏感杂草和抗性杂草分类导致对草甘膦抗性地肤、水麻和豚草的识别准确率分别为88%、93%和92%。