Discriminating Pennisetum alopecuoides plants in a grazed pasture from unmanned aerial vehicles using object‐based image analysis and random forest classifier

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.