Timely and accurate weed detection in pasture is critical for efficient grazing management. Although high‐resolution images from unmanned aerial vehicles (UAVs) offer new opportunities for the detection of weeds at the farm scale, pixel‐based image analyses do not always produce the best results and object‐based image analysis (OBIA) has improved weed discrimination accuracy. In the present study, we evaluated the performance of OBIA on UAV images by integrating random forest (RF) classifier with auxiliary information layers to discriminate and map Pennisetum alopecuroide plants, a prolific and harmful weed, in a grazed pasture. The UAV images were captured at different flight altitudes (28, 56, 82 and 114 m). The OBIA‐RF algorithm included 20 input features: five layers (red‐green‐blue [RGB] or hue‐saturation‐brightness [HSV] image bands, texture and digital surface model) and the descriptive statistics (median, standard deviation, minimum and maximum) for each object. The predicted P. alopecuroides maps were evaluated for out‐of‐bag accuracy and generalized error accuracy in the test dataset. HSV‐based classification had higher classification accuracy, and the lowest altitude of 28 m (spatial resolution, 0.9 cm) was considered the most suitable for the weed detection. Overall, the optimal classification accuracy was achieved in the HSV‐based OBIA‐RF model using the images from the lowest altitude (highest spatial resolution). Among the 20 input features, the brightness information (V layer) in the HSV images was considered the most important because P. alopecuroides ears are black.

中文翻译：

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使用基于对象的图像分析和随机森林分类器，从无人飞行器中鉴别草食草中的狼尾草植物

牧场中杂草的及时，准确检测对于有效管理放牧至关重要。尽管无人驾驶飞机（UAV）的高分辨率图像为农场规模的杂草检测提供了新的机会，但基于像素的图像分析并不总是能产生最佳结果，而基于对象的图像分析（OBIA）改善了杂草的辨别力准确性。在本研究中，我们通过将随机森林（RF）分类器与辅助信息层相结合来区分和映射狼尾草并评估地图，从而评估了OBIA在无人机图像上的性能。放牧的草场中的植物，一种多产且有害的杂草。在不同的飞行高度（28、56、82和114 m）捕获了无人机图像。OBIA-RF算法包括20个输入功能：五层（红绿蓝[RGB]或色相饱和度[HSV]图像带，纹理和数字表面模型）和描述性统计信息（中位数，标准差，最小值和最大）。预测的P. alopecuroides在测试数据集中评估了地图的袋外精度和广义误差精度。基于HSV的分类具有更高的分类精度，最低的海拔高度28 m（空间分辨率为0.9 cm）被认为是最适合杂草检测的地方。总体而言，使用最低高度（最高空间分辨率）的图像在基于HSV的OBIA-RF模型中实现了最佳分类精度。在20个输入特征中，HSV图像中的亮度信息（V层）被认为是最重要的，因为斑节对虾的耳朵是黑色的。